NSGA-II

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Refactoring restructures a program to improve its structure without altering its behavior. However, it is challenging to preserve the domain semantics of a program when refactoring is decided/implemented automatically. Indeed, a program could be syntactically correct, have the right behavior, but model incorrectly the domain semantics. In this paper, we propose a multi-objective optimization approach to find the best sequence of refactorings that maximizes quality improvements (program structure) and minimizes semantic errors. To this end, we use the non-dominated sorting genetic algorithm (NSGA-II) to find the best compromise between these two conflicting objectives. We report the results of our experiments on different open source projects.

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6405292&tag=1

Abstract
In this paper, experimentally derived correlations of heat transfer and pressure drop are used in a Pareto based Multi-Objective Optimization (MOO) approach to find the best possible combinations of heat transfer and pressure drop of TiO2-water nanofluid flow in tubes fitted with multiple twisted tape inserts in different arrangement. In this study there are four independent design variables: the number and arrangement of twisted tape inserts (N), TiO2 volume fraction (φ), Reynolds number (Re) and Prandtl number (Pr). Seven twisted tape arrangement in three different categories are investigated. The objectives are maximizing the non-dimensional heat transfer coefficient (Nu) and minimizing the non-dimensional pressure drop (f Re).
It is shown that some interesting and important relationships as useful optimal design principles involved in the thermal performance of nanofluid flow in tubes fitted with multiple twisted tape inserts in different arrangement can be discovered by Pareto based multi-objective optimization approach.

The primary objective of this work is to introduce the idea of using Pareto optimal solutions (POS) for the
computation of relationships among the factors involved in seismic design of skeletal steel frames. To verify the
capability of this concept, multi-objective optimisation is applied and the relations among total skeletal weight,
resultant seismic base shear, maximum lateral displacement and first eigen period of the structure are investigated.
Due to the importance of structural safety and for economical reasons, due to its intrinsic conflicting manner,
investigation of POS for minimisation of skeletal weight against minimisation of maximum lateral displacement of
the structure is also considered. For this purpose, the fast non-dominated sorting genetic algorithm is applied to
compute POS. After computing related Pareto designs, they are used to determine relationships among design
factors. The design of an ordinary moment-resisting frame is considered under gravitational dead loads and seismic
loads in which lateral earthquake forces are calculated from modal spectral analysis. The structures are under
strength and serviceability constraints. All design requirements correspond to the uniform building code and to
allowable stress design code of practice of the American Institute of Steel Construction. The effectiveness of the
proposed method is verified using some examples.

There is a growing need for scalable search-based software engineering approaches that address software engineering problems where a large number of objectives are to be optimized. Software refactoring is one of these problems where a refactoring sequence is sought that optimizes several software metrics. Most of the existing refactoring work uses a large set of quality metrics to evaluate the software design after applying refactoring operations, but current search-based software engineering approaches are limited to using a maximum of five metrics. We propose for the first time a scalable search-based software engineering approach based on a newly proposed evolutionary optimization method NSGA-III where there are 15 different objectives to be optimized. In our approach, automated refactoring solutions are evaluated using a set of 15 distinct quality metrics. We evaluated this approach on seven large open source systems and found that, on average, more than 92% of code smells were corrected. Statistical analysis of our experiments over 31 runs shows that NSGA-III performed significantly better than two other many-objective techniques (IBEA and MOEA/D), a multi-objective algorithm (NSGA-II) and two mono-objective approaches, hence demonstrating that our NSGA-III approach represents the new state of the art in fully-automated refactoring.

Networked Control Systems (NCSs) often suffer from random packet dropouts which deteriorate overall system's stability and performance. To handle the ill effects of random packet losses in feedback control systems, closed over communication network, a state feedback controller with predictive gains has been designed. To achieve improved performance, an optimization based controller design framework has been proposed in this paper with Linear Matrix Inequality (LMI) constraints, to ensure guaranteed stability. Different conflicting objective functions have been optimized with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The methodology proposed in this paper not only gives guaranteed closed loop stability in the sense of Lyapunov, even in the presence of random packet losses, but also gives an optimization trade-off between two conflicting time domain control objectives.► Predictive state feedback controller is designed to handle packet dropouts in NCSs. ► Multi-objective optimization allows design trade-off between performance measures. ► LMI constraints guarantee Lyapunov stability of NSGA-II based networked controllers. ► Proposed scheme gives Pareto optimal solutions for time-driven controllers in NCSs. ► Compromised solutions are given for three set of time domain conflicting objectives.

Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation of the proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.

In this paper, we investigate the problem of visual coverage in visual sensor networks (VSNs). It is required
to select a subset of sensor nodes to provide a visual coverage over the monitoring region at each point of
time. In contrast with the pervious works which considered only single metric for sensor selection method,
in this study we assumed the sensor selection as multi-criteria problem. For the purpose of maximizing the
network lifetime, we consider three metrics a) visual coverage ratio, i.e., percentage of monitoring region
which is fully covered by camera sensors, b) number of selected sensors, i.e., number of active sensors for
covering the desired region, and c) overlapping coverage ratio, i.e., percentage of monitoring region which
is covered by more than one camera sensor. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is
used to solve the problem. Besides, impact of steady state selection and generational selection method is
studied on the network lifetime. Simulation results show the superiority of multi-objective optimization.
NSGA-II results not only longer network lifetime but also fewer number of active sensor and lower
overlapping ratio at each point of time.

Se presenta el desarrollo de una metodología que incluye un modelo metaheurístico basado en el Algoritmo Genético Ordenado Elitista No-Dominado (Elitist Non-Dominated Sorting Genetic Algorithm, NSGA-II) en un ambiente de producción Job Shop. Esto con el fin de minimizar tres variables fundamentales del proceso: tiempo total de proceso (makespan time), costo de energía y accidentalidad laboral. Con la aplicación de la metodología, se logra optimizar las variables objeto de estudio, en un 42%, respecto a las técnicas tradicionales de programación de la producción. Con base en la aplicación de la metodología propuesta, se plantea explorar otras funciones multi objetivo, en las que se analiza el consumo de otros recursos como agua y combustible, en situaciones no deseadas tales como paro de transportadores, deslizamientos de tierra en carreteras y congestión vehicular.

In this paper, we propose the use of preference-based
evolutionary multi-objective optimization techniques (P-EMO) to
address various software modelling challenges. P-EMO allows
the incorporation of decision maker (i.e., designer) preferences
(e.g., quality, correctness, etc.) in multi-objective optimization
techniques by restricting the Pareto front to a region of interest
easing the decision making task. We discuss the different
challenges and potential benefits of P-EMO in software
modelling. We report experiments on the use of P-EMO on a
well-known modeling problem where very promising results are
obtained.

Developing optimal operating policies for conjunctive use of surface and groundwater resources when different decision makers and stakeholders with conflicting objectives are involved, is usually a challenging task. This problem would be more complex when objectives related to surface and groundwater quality are taken into account. In this paper, a new methodology is developed for real time operating conjunctive use of surface and groundwater resources. In the proposed methodology, in order to develop a Pareto front among objectives, a well-know multi-objective genetic algorithm, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized. The Young conflict resolution theory is also used for resolving the conflict of interests among decision makers. To develop the real time conjunctive use operating rules, the Probabilistic Support Vector Machines (PSVMs), which are capable to provide probability distribution functions of decision variables, are utilized. The proposed methodology is applied to Tehran Aquifer in Tehran metropolitan area, Iran. Stakeholders in the study area have some conflicting interests related to supplying water with acceptable quality, reducing the pumping costs, improving the groundwater quality and controlling the groundwater table fluctuations. In the proposed methodology, MODFLOW and MT3D groundwater quantity and quality simulation models are linked with NSGA-II optimization model to develop Pareto fronts among the objectives. The best solutions on the Pareto fronts are then selected using the Young conflict resolution theory. The selected solution (optimal monthly operating policies) is used to train and verify a PSVM. The results show the significance of applying an integrated conflict resolution approach and appropriate capability of support vector machines for the real time conjunctive use of surface and groundwater resources in the study area. It is also shown that the validation accuracy of the proposed operating rules is more that 80% and based on these rules, the cumulative groundwater table variation is limited to 80 centimetres during a 15-year planning horizon.

This paper presents an application of elitist non-dominated sorting genetic algorithm (NSGA-II) for solving
a multi-objective reactive power market clearing (MO-RPMC) model. In this MO-RPMC model, two
objective functions such as total payment function (TPF) for reactive power support from
generators/synchronous condensers and voltage stability enhancement index (VSEI) are optimized
simultaneously while satisfying various system equality and inequality constraints in competitive electricity
markets which forms a complex mixed integer nonlinear optimization problem with binary variables. The
proposed NSGA-II based MO-RPMC model is tested on standard IEEE 24 bus reliability test system. The
results obtained in NSGA-II based MO- RPMC model are also compared with the results obtained in real
coded genetic algorithm (RCGA) based single-objective RPMC models.

In this paper, a non-dominated sorting genetic algorithm-II (NSGA-II) based approach is presented for distribution system reconfiguration. In contrast to the conventional GA based methods, the proposed approach does not require weighting factors required for conversion of multi-objective function into an equivalent single objective function. . In order to illustrate the performance of proposed method, 33-bus and 69-bus distribution networks have been employed which have led to the desired results.