Optimisation techniques

In modern systems, electrical power distribution and actuation replace mechanical, hydraulic or pneumatic systems. An estimated 30% of world’s electrical energy was processed by power electronic systems in 2005, with a predicted increase to 80% by 2030. An understanding of future capabilities of power electronics & bottlenecks limiting increases in performance of such systems is required. Designing a filter/converter is a complex engineering task that balances many interacting and often conflicting pressures. A computational tool that accounts for them while seeking optimal (power-dense) designs is of high value for designers.

Une simple analyse qui met en lumière la situation présente du secteur de la FTP dans le contexte délicat du long retard dans l'application de la nouvelle législation régissant le secteur, et qui ouvre aussi la voie à une meilleure compréhension des enjeux.

Knee abduction/adduction moment and knee internal rotation moment are known surrogate measures of anterior cruciate ligament (ACL) load during tasks like sidestepping and single-leg landing. Previous experimental literature has shown that a variety of kinematic strategies are associated or correlated with ACL injury risk; however, the optimal kinematic strategies needed to reduce peak knee moments and ACL injury are not well understood. To understand the complex, multifaceted kinematic factors underpinning ACL injury risk and to optimize kinematics to prevent the ACL injury, a musculoskeletal modeling and simulation experimental design was used. A 14-segment, 37-degree-of-freedom, dynamically consistent skeletal model driven by force/torque actuators was used to simulate whole-body single-leg jump landing kinematics. Using the residual reduction algorithm in OpenSim, whole-body kinematics were optimized to reduce the peak knee abduction/adduction and internal/external rotation moments simultaneously. This optimization was repeated across 30 single-leg jump landing trials from 10 participants. The general optimal kinematic strategy was to bring the knee to a more neutral alignment in the transverse plane and frontal plane (featured by reduced hip adduction angle and increased knee adduction angle). This optimized whole-body kinematic strategy significantly reduced the peak knee abduction/adduction and internal rotation moments, transferring most of the knee load to the hip.

The synthesis of heat exchanger networks (HENs) has mainly been done through the use of approximate models for each of the individual heat exchangers that comprises the network. These approximate models do not adequately take into account key parameters such as the overall heat transfer coefficient , TEMA standards, pressure drops, FT correction factors, and multiple shells. These factors can significantly alter the cost of the network. This paper presents a new methodology for the synthesis of heat exchanger networks using detailed heat exchanger design models that takes into account the aforementioned design parameters. The newly developed method involves the following steps. First, a SYNHEAT (Yee and Grossmann, 1990) MINLP model is solved. The individual exchangers for the resulting network are then designed using heuristics, TEMA standards and the Bell-Delaware method. From the designs obtained for these individual exchangers, correction factors are inserted into the SYNHEAT model that account for changes in overall heat transfer coefficient, TEMA choices, pressure drops, Ft correction factors and the effect of multiple shell passes. The SYNEAT model is then rerun and individual exchangers redesigned and the procedure repeated until convergence is achieved. For each iteration the change in each correction factor is limited to avoid the omission of certain solutions. While the methodology cannot guarantee global optimality it can ensure that the synthesised processes are physically achievable and has also been shown to converge on physically meaningful parameters without the explicit formulation of complicated non-linear equations in the MINLP formulation.

Network optimisation techniques are easy to use and can solve large optimization problems quickly when compared with techniques such as Linear Programming. By the early 1980s, network optimization techniques had been developed, extended, and applied to a far wider range of problems than the earlier Out-of-Kilter network optimization algorithm. For example, through the incorporation of LP-like side constraints, and flow gains and losses. This paper describes these developments from the perspective of the operations research/ modelling practitioner rather than the algorithmist. Several practical applications to agribusiness and the criminal justice system are used to illustrate the capability of the new network optimisation algorithms.

Citation:
Kearney, T. D., Hall, K. R., & Mellalieu, P. J. (1984). Recent Advances in Network Optimization Methods and Applications. In Proceedings of the Annual Conference of the United Kingdom Operational Research Society. Retrieved from http://unitec.academia.edu/PeterMellalieu/Papers/1569500/Recent_Advances_in_Network_Optimization_Methods_and_Applications

h i g h l i g h t s PV -Battery system under FiT incentive is modelled to maximise revenue streams. Optimisation model is developed to optimise power flows in the PV-Battery system. Real residential PV and demand data is used to simulate the optimisation model. Sensitivity analysis on the impact of battery capacity on the model is carried out. Impact of unit cost on the adoption of battery storage for PV under FiT is studied.

Optimisation has attracted the attention of human since ancient times, during which the activities were inconsiderable and resources were unlimited. Extension of activities and their cost, time and resources limitations have caused researchers pay their attention to optimising the activities. Optimum alternative should be feasible, economical, functional and effective as possible. "Structural optimisation" is the technical term for the application of optimisation on structures. The last decades has seen a rapid development in optimisation techniques to solve related problems in structural design. However, recent surveys have shown insufficient penetration of optimisation methods in concrete structures. Due to the complexities of the design and construction of structures, optimisation techniques faced serious obstacles until the modern (meta-heuristic) methods of optimisation were developed. Applying such methods became quite widespread in various problems, including those of concrete structures, the optimisation of which were highly problematic and sometimes impossible before the introduction of these methods. The goal of concrete structure optimisation is often the minimization of the costs including the costs of concrete material, reinforcing bars, formwork, and pre-stressed cables. In a limited number of cases, the geometry and shape of structures were optimised. In this paper, an updated and extensive literature review is presented and due to the wide variety of meta-heuristic algorithms, appropriate concrete structure optimisation algorithms for practical practitioner are addressed. This research illustrated shortage of the concrete structure optimisation cases, researches and required direction of future studies in this regards. Researchers have been seeking the best responses to human problems for ages and optimization has been utilized as a set of principles and approaches to find the most appropriate answer in this regard. Approaches applied in optimization of complex problem have been developed through knowledge improvements. Meta-heuristics algorithms (MAs) are the latest category of approaches used by researchers to optimize complex and non-linear polynomial (NP-Hard) problems. These approaches are not able to provide global optimum, but they provide an acceptable solution for problems at a reasonable cost in a short time. Main difference of various meta-heuristic methods is their ability to solve certain types of problems and achieve closer answer to the global optimum at a lower cost in a shorter time (Cercevic,2014). Today, application domain of optimization techniques has been spread due to the development of knowledge and computer numerical analysis techniques; so that optimization objectives have changed from one target to multiple targets and subjects of problems have got closer to the reality. In primary problems, the main purpose of optimization solving problem was finding the minimum weight, while structural optimization is not only aimed at the weight and factors such as construction cost, time and quality, different construction methods, etc. could also be considered as objects. The development and many introduced techniques have caused a difficulty in selecting appropriate MAs to optimize a problem. Lots of existing methods (Figure 1), the diversity of their names (Table 1) and the provision of new algorithms have made the user confuse while solving a optimization problem; so that selection of appropriate algorithm for a

The study compared the performance of students in Chemistry when taught with guided discovery and demonstration teaching techniques in senior secondary schools. It determined the effectiveness of guided discovery and demonstration teaching techniques in enhancing male and female students’ performance in Chemistry. The study adopted a non-equivalent pre-test, posttest control group research design. The population for the study consisted of Chemistry students in senior secondary schools in Ile–Ife. The sample consisted of Eighty four (84) students from three schools in Ife Central Local Government Area of Osun State which were selected using simple random sampling technique. One intact class each was used. The schools were randomly assigned to three groups (Guided Discovery, Demonstration and Teacher Expository) using simple random sampling technique. Two instruments were utilized for the study. They were Chemistry Achievement Test (CAT) and Questionnaire on Attitudes of Students towards Chemistry (QASTC). Data collected were analyzed using Analysis of Covariance (ANCOVA) and Analysis of Variance (ANOVA).The results showed that, there was a significant difference in the performance of students exposed to guided discovery, demonstration teaching technique and those exposed to teacher expository teaching technique (F= 123.972 ; p< 0.05). The findings also showed that, there was a significant difference in the performance of male and female Chemistry students exposed to guided discovery and demonstration teaching techniques (F= 12.04; p< 0.05). This showed that male and female students performed better when exposed to guide discovery teaching techniques. The post-hoc analysis revealed that, Guided Discovery Teaching Technique (GDTT) had a better significant effect on student academic performance than Demonstration Teaching Technique (DTT) and Teacher Expository Teaching Technique (TETT). The post - hoc analysis revealed that, male and female students exposed to GDTT performed better than those exposed to DTT and that, those exposed to GDTT had the best retention ability of the concept taught. The study concluded that, guided discovery teaching technique is a better teaching technique on students’ performance in Chemistry than demonstration teaching technique and the teacher expository method respectively.

Cette communication entend traiter de la portée technique et pratique de la théorie (philosophique) de l’imagination que Gilbert Simondon a notamment formulée dans son cours (de psychologie) à la Sorbonne en 1965-1966. [Simondon 2008]
Avant tout, il convient de rappeler que cette théorie de l’imagination est réaliste et affirme une pluralité des modes d’existence des images, jusqu’à comprendre les concrets artefacts humains (les inventions) dans la classe des images, en affirmant qu’il existe une relation directe (et réelle) entre l’imagination et la réalité de l’idéation des artefacts humaines dans toute leur évolution technique et esthétique. L’évolution des techniques — de ce point de vue réaliste — montre le côté objectif, indéniable et cumulatif de l’idéation.  Cette objectivité de l’idéation est fondée sur la réalité a posteriori des « idées » entendues comme « modèles » et « objets sociaux » — au sens de ce qu’aujourd’hui on appelle « textualisme faible » (Ferraris 2009) — constituants de ce que — au sens de la sémiotique de Umberto Eco – on appelle « Encyclopédie » d’une culture (Eco 1999).
Bien que Simondon n’emploie pas (explicitement) le terme « eidos » et une perspective « textualiste » ou « sémiotique », il nous donne une complète ontologie des objets techniques (ex. Simondon 1958), c’est-à-dire, une vision capable aussi de décrire ce que nous appellerons — en empruntant un terme du lexique zoologique du grec moderne — « eidogenesis [spéciation] des artefacts ». En ce sens, l’Eidogenesis est une question qui se situe entre nature et culture, au niveau des processus (psychologiques et anthropologiques) de la catégorisation, au moins dans les aspects ou les catégories sont des véritables objets sociaux et non seulement des images mentales ou de concepts privés.
En ce sens la théorie de l’imagination technique touche l’ontologie des objets. Et, connu surtout en tant que penseur de la technologie e de l’ontologie, Simondon, dans l’ensemble de son œuvre, propose aussi l’esquisse très embryonnaire d’une « technologie de l’imagination » pas encore bien exploitée en tant que telle ; il nous offre une vision qui pourrait — à notre sens — constituer un point de référence pour les étudies (historiques, techniques et anthropologiques) sur la conception des artefacts. Cette “technologie” peut nous offrir une idée claire des idées travaillées dans la concrète idéation technique et esthétique. En revanche, le monde des artefacts — de sa réalité matérielle — est la pierre de touche de l’adéquation des théories (philosophiques, anthropologiques et psychologiques) de l’imagination.
Donc, une théorie technique de l’imagination est questions dans laquelle on doit se croiser e se traduire entre eux les points de vue des philosophes et des concepteurs, les modèles technologiques de l’ontologie et les modèles ontologiques de la technologie (Simondon 2011).
Pour aborder cette question d’une façon à la fois concrète et générale, il nous faut donner un exemple tiré de la modélisation scientifique et technique ; nous choisissons de traiter le thème de l’imagination e de la catégorisation eidétique que le modèle morphogénétique et physicaliste de Alan Turing (1952) a propagée soit en biologie, soit dans la fabrication des objets à fonction esthétiques.

Contamination emergency in water distribution systems is a complex situation where optimal operation becomes important for
public health. In case of emergency corrective operational actions for flushing the pollutant out of the network are needed, which have to
be fast and accurate. Under such a stressful situation, trial-and-error simulation experiments with the hydrodynamic and water quality
models cannot be applied since significant number of model evaluations may be required to identify the optimal solution. This paper
presents a methodology for finding sets of operational interventions in a supply network for flushing a contaminant by minimizing the
impact on the population. The situation is treated as both single- and multiobjective optimization problem, which is solved by using
evolutionary optimization approaches, in combination with the EPANET solver engine. The methodology is tested on a simple imaginary
network configuration, as well as on a real case study for the city of Villavicencio in Colombia. The results prove the usefulness of the
approach for advising the operators and decision makers.

In October 2015, the OECD/G20 presented their final report on the Base Erosion and Profit Shifting (BEPS) Project. This article presents a unique analysis of the OECD/G20's recommendation on Action 4 by utilising tax optimisation modelling to simulate and examine a hypothetical multinational enterprise's (MNE's) behavioural response to this recommendation.

This paper considers dimensional synthesis of a six-bar linkage with turning kinematic pairs, in the literature known as Stephenson III Six-bar linkage. The synthesis procedure started from the requirement that it should be a double dwell mechanism and that the coupler point, during dwell, should pass through the given points belonging to the circular arc. The coordinates of those points are not directly given; they lie on the circular arc defined by the corresponding centre of the curve and the crank angle that corresponds to the circular arc of the path during dwell. These values are obtained at the end of the optimization procedure so that in this case the precision points change their positions with the change of the position of the arc on which they lie. As this is the case with double dwell, the coupler point should describe a curve containing two circular arcs which does not have to be symmetric. A new metaheuristic algorithm, known as Cuckoo Search (CS), was used in the procedure of optimum synthesis of mechanism parameters. j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / m e c h m t Diversification via randomization avoids the solutions being trapped at local optima and, at the same time, increases the diversity of the solutions. The good combination of these two major components will usually ensure that the global optimality is achievable.

— Optimization techniques are very well-known to improve the performance of Three-phase induction motor (TIM). Inverter and SVPWM method can be used for setting voltage and frequency appropriating with loads requirements. This study deals with the tuning of PID controller parameters to be used TIM. Genetic algorithm is used to tune each parameters of PID speed controller to improve the speed response performance of the TIM. PID-GA aims to minimize the error signal speed of TIM, reduce overshoot, and tune parameters of PID controller. The error signal speed of TIM has objective function from integrated error signal (ISE). Parameters of PID controller from optimization is used to the model of the closed-loop speed control of three-phase induction motor using PID. PID-GA has the best speed response. On the model of the closed-loop speed control of three-phase induction motor using PID, various load torque can result in decreasing flux stator and rotor, increasing current stator and rotor, and decreasing speed of the induction motor.

An article that reviews the work of Kantorovich in the light of von Mises claim that rational calculations were impossible without markets. It gives a tutorial introduction to the use of Kantorovich's methods, compares his approach to that of Dantzig. An assesment is given of the extent to which new interior point methods of linear programming strengthen or weaken Kantorovich's claims.

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that have been used to solve complex optimization problems that the traditional techniques finds very difficult to solve. The Interior-Point Methods (IPMs) are efficient tools for solving nonlinear optimization problems. The IPMs having constrains that are active at the current point, are now believed to be the most robust algorithms for solving large-scale nonlinear optimization problems. Though they are very efficient, but they are still plagued with several challenges such as how to handle of non-convexity, the procedure for making the barrier constraint up to date is cumbersome despite the existence of nonlinearities, and the need to ensure progress toward the solution. In order to overcome some of the shortcomings of the standard PSO such as premature convergence and particles been trapped at the local minimal, we proposed the Primal-Dual Interior Point Particle Swarm Optimization (pdipmPSO) to surmount the shortcomings of the original PSO. We applied the Primal Dual to each particle in a finite number of iterations, and feed the PSO with the output of the Primal Dual. We compared the performance of our new algorithm (pdipmPSO) with IPM and PSO using 13 different benchmark functions. Optimization results reveal that pdipmPSO performs better than PSO and IPM. Our proposed algorithm is shown to have great capacity to prevent premature convergence, and the curse of particles being trapped in the local minimal which have characterised many variants of PSO.

Contemporary organizations rely on outsourcing for success in today’s competitive marketplace, and selecting a vendor is an important process as developing new products. Vendor selection is one of the most important decisions of purchasing function. As organizations become more dependent on vendors, the direct and the indirect consequences of poor decision-making become more severe. Literature shows many vendor evaluation models. In this paper we have proposed a vendor selection model using Integer Linear Programming (ILP) Model for multi-product, multi-vendor environment. The contribution of this research lies in the implementation of this model as a customized decision support system according to the expectation of any company. The model is validated with a case study by implementing the model for Agricultural equipments whole sale company.

Stochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm intelligence algorithms. In contrast to many nature-inspired algorithms, stochastic diffusion search has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews various developments of the algorithm, which have been shown to perform well in a variety of application domains including continuous optimisation, implementation on hardware and medical imaging. This algorithm has also being utilised to argue the potential computational creativity of swarm intelligence systems through the two phases of exploration and exploitation.

Flexible job shop scheduling is a hard combinatorial optimization problem. This paper introduces a simulation-based Genetic Algorithm approach to solve flexible job shop scheduling problem. Four manufacturing scenarios have been considered to access the performance of a job shop with objective to minimize mean tardiness, mean flow time and makespan. Results show that multiple process plans performs better than single process plan for each job type and if only single process plan is made available, then process plan selected on the basis of minimum production time criterion yields better results than other criterion of randomly selected process plan and minimum number of set-ups. Moreover, embedding restart scheme into regular Genetic Algorithm results improvement in the fitness value.

Mathematical and computer-based models provide the foundation of most methods of engineering design. They are recognised as being especially important in the development of integrated dynamic systems, such as “control-configured” aircraft or in complex robotics applications. These models usually involve combinations of linear or nonlinear ordinary differential equations or difference equations, partial differential equations and algebraic equations. In some cases models may be based on differential ...

Modeling uncertainty during risk assessment is a vital component for effective decision making. Unfortunately,
most of the risk assessment studies suffer from uncertainty analysis. The development of tools
and techniques for capturing uncertainty in risk assessment is ongoing and there has been a substantial
growth in this respect in health risk assessment. In this study, the cross-disciplinary approaches for
uncertainty analyses are identified and a modified approach suitable for industrial safety risk assessment
is proposed using fuzzy set theory and Monte Carlo simulation. The proposed method is applied
to a benzene extraction unit (BEU) of a chemical plant. The case study results show that the proposed
method provides better measure of uncertainty than the existing methods as unlike traditional risk analysis
method this approach takes into account both variability and uncertainty of information into risk
calculation, and instead of a single risk value this approach provides interval value of risk values for a given
percentile of risk. The implications of these results in terms of risk control and regulatory compliance's are also discussed.

The integration of renewable energy sources together with an energy storage system into a distribution network has become essential not only to maintain continuous electricity supply but also to minimise electricity costs. The operational costs of this paradigm depend highly upon the optimal use of battery energy. This paper proposes day-ahead scheduling of the battery energy while considering its degradation costs due to charging-discharging cycles. The degradation costs with respect to the depth of charge are modelled and added to the objective function to determine the actual operational costs of the system. A framework to solve the function is developed in which particle swarm optimisation, the Rainflow algorithm and scenario techniques are integrated. Uncertainties of parameters, modelled by scenario generation and reduced by scenario reduction techniques, are discussed. Simulation results demonstrate that the proposed method can reduce the operational costs by around 40% compared to the baseline method. They also reveal that uncertainty in power generation and power demand has no influence on the energy schedule of the battery, but variation in electricity prices has an impact on the outcome. Several pragmatic tests verify the effectiveness of the proposed method. Keywords: Community microgrids, scheduling battery energy, battery degradation costs, renewable energy and optimisation algorithms. Nomenclature RESs Renewable energy sources EMS Energy management system TOU Time-of-use RTP Real-time electricity price ESS Energy storage system ADP Approximate dynamic programming PSO Particle swarm optimisation HBB-BC Hybrid big bang big crunch SCADA Supervisory control and data acquisition SI Solar irradiation PV Photovoltaic P wT Total wind power P sT Total solar power P g Import/export grid power C(t) Grid electricity prices L(t) Load at time t BL o Initial battery energy level BL max Maximum battery energy level BL min Minimum battery energy level P c,max Maximum charging rate

This paper describes the design of a non-axisymmetric hub contouring in a shroudless
axial flow compressor cascade operating at near stall condition. Although, an optimum
tip clearance reduces the total pressure loss, further reduction in the loss was achieved
using hub contouring. The design methodology presented here combines an evolutionary
principle with a three-dimensional CFD flow solver to generate different geometric
profiles of the hub systematically. The resulting configurations were preprocessed by
GAMBIT© and subsequently analyzed computationally using ANSYS Fluent ©. The total
pressure loss coefficient was used as a single objective function to guide the search
process for the optimum hub geometry. The resulting three dimensionally complex hub
promises considerable benefits discussed in detail in this paper. A reduction of 15.2%
and 16.23% in the total pressure loss and secondary kinetic energy, respectively, is
achieved in the wake region. An improvement of 4.53% in the blade loading is observed.
Other complimentary benefits are also listed in the paper. The majority of the benefits
are obtained away from the hub region. The contoured hub not only alters the pitch-wise
static pressure gradient but also acts as a vortex generator in an effort to alleviate the
total pressure loss. The results confirm that non-axisymmetric contouring is an effective
method for reducing the losses and thereby improving the performance of the cascade.

A systematic investigation into the design and simulation of flow parameters in a closed-loop wind tunnel was carried out using Computational Fluid Dynamics (CFD). The analytical model for estimating pressure losses were directed as input boundary conditions. Full-scale model of the entire wind tunnel was considered instead of the conventional approach, in which only test section flow is simulated. This allowed for optimisation of flow quality not only in the test section but also the flow in the entire circuit. Analysis of the guide vane configurations showed that test section flow quality was more affected by flow conditions in upstream than downstream sections. Hence, special attention must be given while designing the vanes at upstream turns particularly corners in line with the test section. Validation of the test section with block model showed that CFD was able to replicate wind tunnel measurements of velocity, turbulence intensity and pressure coefficient with error below 10%.

Duplex stainless steels are extensively used in chemical industries due to its superior corrosion resistance, high strength and toughness properties after welding. During drilling the degree of burr height and burr thickness of the duplex material plays a significant role, though any irregularities will reduce the final quality of the product. Because deburring processes are not yet well automated, understanding of the burr development in drilling and its principal parameters is important for controlling the burr sized at the fabrication stage itself. Design of experiments, Box-Behnken design have been used to study the effects of the drilling parameters such as spindle speed and feed rate (input data’s) used on the prediction of burr height and burr thickness (output response) on drilling Duplex 2205. This work reveals the development of mathematical models to predict the tool exit portion burr height and burr thickness, which were used to determine the characteristics of the material. Experiments were performed under different drilling conditions of spindle speed and feed rate. Residual plots were constructed to analyze the variation between the experimental values and model values. Response contours were constructed to determine the optimum drilling condition for the burr height and burr thickness. The developed model establishes a good correlation between spindle speed and feed rate that influences the burr height and burr thickness. Analysis of Variance (ANOVA) was employed to identify the level of importance in drilling parameters on their performance characteristics. The ANOVA showed better coefficient of determination (R2) value of 0.8726 and 0.853 for burr height and burr thickness respectively, thus ensuring a satisfactory adjustment of the second order regression model with experimental data. The performance evaluation of the mathematical model was analyzed, ensuring good correlation over the actual data’s. The optimum value of cutting parameters is also determined to get minimum values burr height and burr thickness.

In the aerospace industry, More Electric Aircraft (MEA) applications require high power-density aiming to reduce weight, complexity, fuel consumption, gas emissions, noise, and operational costs. Likewise, environmental impact is reduced in the automotive industry by employing electric powertrains alongside internal combustion engines in HEVs. Thus, designing power-dense converters is paramount. This work aims to develop computationally tractable physics-based thermal models of DC-DC converters suitable for design optimisation, as well as an optimisation framework and a user-friendly graphical interface to facilitate the use of the software design tool by the end-user.

The weapon target assignment (WTA) problem has been designed to match the Command & Control (C2) requirement in military context, of which the goal is to find an allocation plan enabling to treat a specific scenario in assigning available weapons to oncoming targets.
The WTA always get into situation weapons defending an area or assets from an enemy aiming to destroy it. Because of the uniqueness of each situation, this problem must be solved in real-time and evolve accordingly to the aerial/ground situation. By the past, the WTA was solved by an operator taking all the decisions, but because of the complexity of the modern warfare, the resolution of the WTA in using the power of computation is inevitable to make possible the resolution in real time of very complex scenarii involving different type of targets. Nowadays, in most of the C2 this process is designed in order to be as a support for a human operator and in helping him in the decision making process. The operator will give its final green light to proceed the intervention.

Flexible job shop scheduling is a hard combinatorial optimization problem. This paper introduces a simulation-based Genetic Algorithm approach to solve flexible job shop scheduling problem. Four manufacturing scenarios have been considered to access the performance of a job shop with objective to minimize mean tardiness, mean flow time and makespan. Results show that multiple process plans performs better than single process plan for each job type and if only single process plan is made available, then process plan selected on the basis of minimum production time criterion yields better results than other criterion of randomly selected process plan and minimum number of set-ups. Moreover, embedding restart scheme into regular Genetic Algorithm results improvement in the fitness value.

The corporation is currently operating in a hyper-connected world in which scores of heterogeneous devices are constantly sharing information in a variety of application contexts such as wellness, improved communications, digital companies, and so on. However, in this case, the wider the genuine wide range of devices and connections, the greater the risk of security risks. Network Intrusion Detection Systems (NIDSs) will be the most popular line of defence in communications networks to combat malicious behaviour and preserve important security services. Nonetheless, there is no standard process for evaluating and comparing NIDSs. Almost all of the ideas fail to disclose critical NIDS validation procedures, making comparison difficult, if not impossible. In this research, an optimization-based method for detecting Botnet attacks in IoT environments is proposed. Botnet detection based on the Genetic Algorithm is proposed, with dynamic thresholds depending on the GA.

Technology developments in the field of modular data links may allow the creation of a multi-link communication network to be established between anti-air missiles and the launch platform. The future prospect of such ad hoc networks with many existing guidance and allocation schemes makes it possible to consider cooperative strategies for the air defence. Over the past decades, a range of missile genres have been developed mostly on the basis of one-on-one engagements which are then optimized for many-on-many scenarios. A priori allocation rules and natural missile dispersion can allow a salvo of missiles to engage a swarm of targets; however, this does not always avoid some targets leaking through the salvo, whilst other targets may experience overkill. Therefore, weapon target assignment places greater demands on the guidance chain compared with the one-on-one engagements. This study addresses a dynamic weapon target assignment (DWTA) problem in which defending weapons must protect an area against attacks from oncoming aerial threats. In order to improve the performance and efficiency of DWTA, we propose a two-step optimisation method which combines different optimisation approaches such as graph theory, evolutionary game theory (EGT), and particle swarm optimisation (PSO). The optimal DWTA problem considered in this study is to find the allocation policy enabling the area air defence against the oncoming threats in an optimal fashion. The goal is not only to destroy all the threats, but also to avoid exposing danger to own assets such as the defending weapons. Therefore, the proposed algorithm endeavours to intercept the threats as early as possible in selecting the assignment offering the more of possible date to fire. This algorithm enables to minimise the overflight inside the protected area as well.

Bat Algorithm (BA), is a relatively new nature inspired metaheuristic algorithm, which works on the echolocation capabilities of micro-bats. Although being highly efficient, it suffers from pre-mature convergence. To overcome this limitation, this paper proposes a multimodal variant of BA, called Multi-Modal Bat Algorithm (MMBA), which includes the foraging behaviour of bats. The standard BA exhibits a random movement for catching its prey. This work also proposes an enhancement to these exploration capabilities of bat, called Bat Algorithm with Improved Search (BAIS). Each of these variants is tested for its efficacy against BA over 30 benchmark functions. An integration of both these modifications, the Multi-Modal Bat Algorithm with Improved Search (MMBAIS), is also subsequently compared against the same 30 benchmark functions. Results established the superiority of MMBAIS over BA. Experimental comparison of MMBAIS with a recent variant of BA also revealed the efficiency of MMBAIS.

The growing customisation capabilities of services, especially in Cloud scenarios, have led to the so-named Highly-configurable Services (HCSs). Such capabilities are boost- ing the demand and complexity of HCS-based applications and the infrastructure need to support them, HCS-driven solutions from now on. After a study of the existing literature we conclude that these HCS-driven solutions can be significantly enhanced in both 1) the languages to describe the configurations, a.k.a. the decision space of HCSs and 2) the techniques to extract useful information from the decision space, analysis techniques, in advance.
On the one hand, there are no Domain Specific Languages (DSLs) to describe the decision space, although there exist some very close approaches. We suggest that it is possible to improve the current landscape by designing a DSL: i) Compliant with big HCS vendors, ii) multi-item aware, iii) expressive-enough to ease the description of arithmetic-logical relationships on configurable description terms, and iv) domain-independent. In addition, this DSL must define validity criteria for checking that the decision space satisfies some basic properties such as the consistency, and the configurability. Furthermore, explanations must be provided when the decision space do not satisfy such basic properties.
On the other hand, most of the current analysis techniques such as those relate to find optimal configurations or reconfigure a multi-tenant service includes some draw-backs that can be found in emerging techniques. To overcome such drawbacks there must be developed: a) fully-functional reference implementations, b) techniques with a reuse-oriented design, and c) user-friendly interfaces.
The main goal of this dissertation is to enhance the existing support to develop HCS-driven solutions considering the aforementioned suggestions for improvement. The main contributions are a DSL to specify the decision space of HCSs called SYNOP- SIS, and a novel catalogue of analysis operations to check and explain validity criteria as well as to find optimal configurations for one or many users. As minor contributions, two solutions have been developed to improve the existing tooling support to migrate on-premise infrastructure to Amazon and to reconfigure multi-tenant service.
The cornerstone of our proposal to improve the specification techniques has been to define a DSL, SYNOPSIS, and endow it with a formal semantics based on Stateful Feature Models. Regarding our proposal to improve the analysis techniques, the key has been the organisation of such techniques in a catalogue of basic analysis operations that can be combined to support more advanced HCS-driven solutions. The applicability of our results is limited to those decision spaces that can be translated to a Stateful Feature Model, that is enough to support real-world HCSs, in our experience.

to create a model from geometrical data, so as to solve the optimisation problem. An
optimum design procedure is conducted in the paper, and the total material volume of
the bridge substructure is adopted as an objective function. The substructure includes
the column and the reinforced-concrete arch. Finally, the optimization technique
is implemented using the Simultaneous Perturbation Stochastic Approximation
algorithm.

This paper describes the first application of the Bees Algorithm to mechanical design optimization. The Bees Algorithm is a search procedure inspired by the way honey bees forage for food. Two standard mechanical design problems, the design of a welded beam structure and the design of coil springs, were used to benchmark the Bees Algorithm against other optimization techniques. The paper presents the results obtained showing the robust performance of the Bees Algorithm.

In October 2015, the OECD/G20 presented their final report on the Base Erosion and Profit Shifting (BEPS) Project. This article presents a unique analysis of the OECD/G20’s recommendation on Action 4 by utilising tax optimisation modelling to simulate and examine a hypothetical multinational enterprise’s (MNE’s) behavioural response to this recommendation. The literature to date has primarily focused on the “debt bias”, which arises from the distortion in the tax treatment between debt and equity financing. The BEPS Project is no exception, despite acknowledging that the “mobility and fungibility of money makes it possible for multinational groups to achieve favourable tax results”, the focus has remained on the debt bias. Prior work by the author introduced a broader conception of funding biases; specifically, the tax‑induced cross‑border “funding bias”. The funding bias includes intercompany licensing and leasing activities in addition to debt and equity financing. These four form...

Due to the rapid growth of multimedia in network technology, accessing the digital media becomes very easy. Hence, protecting the intellectual property requires more interest in image watermarking. For this sake, different image watermarking approaches are developed, but it poses robustness and transparency issues. Therefore, an effective image
watermarking method named Water Chaotic fruit fly Optimization algorithm-based Deep Convolutional neural network (WCFOA-based Deep CNN) is developed for embedding the secret message to the cover media. The proposedWCFOA is developed by integrating theWater Wave Optimization (WWO) with the Chaotic Fruit Fly Optimization algorithm
(CFOA). The inspiration of propagation operator and the refraction operator increases the diversity of population and minimizes the premature convergence. However, the breaking, propagation and the refraction operator of the proposed optimization shows the
effectiveness of balance between the exploitation of exploration phase in search space using the fitness measure. Accordingly, the embedding process is achieved using the wavelet transform with the selected optimal region using the evaluated fitness value. Several images of brain tumors from BRATS dataset, with tumors having different contrast and form, are used to assess the proposed method. The experimental analysis shows that, the proposed WCFOA-based Deep CNN obtained better performance using the metrics, like correlation coefficient and Peak signal-to-noise ratio (PSNR) with the values of 1 and 45.2157 using without noise scenario and the correlation coefficient and PSNR of 0.9918 and 45.0627 for Impulse noise. By considering the salt and pepper noise, the correlation coefficient and PSNR is 0.9918 and 47.001 and in the Gaussian noise scenario the values of correlation coefficient and PSNR is 0.990 and 46.985, respectively

Among the several optimization techniques available today, Group search optimizer
(GSO) which draws inspiration from the animal foraging behavior, adopting the
producer–scrounger (PS) model has been applied to several optimization problems. In
Traditional GSO, a single population consisting of producers, scroungers and rangers
have been employed. In this paper, a modified GSO with multiple sub populations
(MPGSO) is presented which enhances the performance of the basic GSO. Twenty
benchmark functions are used to evaluate the performance of the proposed algorithm.
Experimental results prove the fact that the MPGSO is able to achieve better results
than the basic GSO.

An integrated design approach for the cost and embodied carbon optimisation of reinforced concrete structures is presented in this paper to inform early design decisions. A BIM-based optimisation approach that utilises Finite Element Modelling (FEM) and a multi-objective genetic algorithm with constructabil-ity constraints is established for that purpose. A multilevel engineering analysis model is developed to perform structural layout optimisation, slab and columns sizing optimisation, and slab and columns reinforcement optimisation. The overall approach is validated using real buildings and the relationships between cost and carbon optimum solutions are explored. The study exhibits how cost effective and carbon efficient solutions could be obtained without compromising the feasibility of the optimised designs. Results demonstrate that the structural layout and the slab thickness are amongst the most important design optimisation parameters. Finally, the overall analysis suggests that the building form can influence the relationships between cost and carbon for the different structural components.