Heuristic Optimization

This paper presents extensive computational experiments to compare 10 heuristics and 20 metaheuristics for the maximum diversity problem (MDP). This problem consists of selecting a subset of maximum diversity from a given set of elements. It arises in a wide range of real-world settings and we can find a large number of studies, in which heuristic and metaheuristic methods are proposed. However, probably due to the fact that this problem has been referenced under different names, we have only found limited comparisons with a few methods on some sets of instances. This paper reviews all the heuristics and metaheuristics for finding near-optimal solutions for the MDP. We present the new benchmark library MDPLIB, which includes most instances previously used for this problem, as well as new ones, giving a total of 315. We also present an exhaustive computational comparison of the 30 methods on the MDPLIB. Non-parametric statistical tests are reported in our study to draw significant conclusions.

The optimal fractional system identification is a challenging problem as it requires the estimation of not only the numerator parameters, but also the poles transfer function model and the non-integer order reading to complex nonlinear optimization. In this paper, an algorithm using the least square method, called ''Vector Fitting" (V.F.) developed by Gustavsen is extended to the fractional order system identification in frequency domain. This algorithm proceeds recursively contrarily to the well known Levy's algorithm which uses only one iteration to calculate the model parameters. The use of an iterative method efficiently directs the model parameters evolution towards their optimal values. Indeed, during iteration the poles of the model are calculated and used as starting poles for the next one, the stability of the identified model can thus be imposed. The V.F. algorithm is then associated with the heuristic optimization method: particle swarm optimization (PSO), leading to a new fractional system identification algorithm. The algorithm works in a hierarchical way; in a higher level, PSO determines the non-integer order and in a lower stage, the V.F. algorithm identifies the other parameters.

This paper considers the problem of determining the locations of wind generators in a wind farm consisting of many generators. The objective is to find a generator placement that maximizes profit, which is the product of the cost efficiency of the generators and the total power output from the wind farm. Generator placement is significant because if generator A is located close to generator B and is located downwind of generator B then the power output of generator A is reduced by an amount that varies with the distance between the generators. The problem can be formulated using mathematical programming but to solve the problem one cannot employ traditional optimization methods. Therefore, a greedy improvement heuristic methodology is developed and described in detail. The effectiveness of the proposed heuristic is demonstrated on a suite of test problems. These results indicate that the proposed method represents an effective solution strategy for this problem.

20 The Dynamically Dimensioned Search (DDS) continuous global optimization algorithm by 21 Tolson and Shoemaker [2007] is modified to solve discrete, single-objective, constrained Water 22 Distribution System (WDS) design problems. The new global optimization algorithm for WDS 23 optimization is called Hybrid Discrete Dynamically Dimensioned Search (HD-DDS) and 24 combines two local search heuristics with a discrete DDS search strategy adapted from the 25 continuous DDS algorithm. The main advantage of the HD-DDS algorithm compared with other 26

This paper proposed a penalty guided artificial bee colony algorithm (ABC) to solve the reliability redundancy allocation problem (RAP). The redundancy allocation problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g. the total cost. RAP has been an active area of research for the past four decades. The difficulty that one is confronted with the RAP is the maintenance of feasibility with respect to three nonlinear constraints, namely, cost, weight and volume related constraints. In this paper nonlinearly mixed-integer reliability design problems are investigated where both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. The reliability design problems have been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. To the best of our knowledge the ABC algorithm can search over promising feasible and infeasible regions to find the feasible optimal/near-optimal solution effectively and efficiently; numerical examples indicate that the proposed approach performs well with the reliability redundant allocation design problems considered in this paper and computational results compare favorably with previously-developed algorithms in the literature.

Purpose: To present an automated technique for two-field tangential breast intensity-modulated radiotherapy (IMRT) treatment planning. Method and Materials: A total of 158 planned patients with Stage 0, I, and II breast cancer treated using wholebreast IMRT were retrospectively replanned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle 3 ) and are designed to perform the manual volume delineation, beam placement, and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radio-opaque markers placed at CT simulation as inputs, optimizes the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole-breast volume. The IMRT parameters are optimized according to the automatically delineated whole-breast volume. Results: The mean time to generate a complete treatment plan was 6 min, 50 s ± 1 min 12 s. For the automated plans, 157 of 158 plans (99%) were deemed clinically acceptable, and 138 of 158 plans (87%) were deemed clinically improved or equal to the corresponding clinical plan when reviewed in a randomized, double-blinded study by one experienced breast radiation oncologist. In addition, overall the automated plans were dosimetrically equivalent to the clinical plans when scored for target coverage and lung and heart doses. Conclusion: We have developed robust and efficient automated tools for fully inversed planned tangential breast IMRT planning that can be readily integrated into clinical practice. The tools produce clinically acceptable plans using only the common anatomic landmarks from the CT simulation process as an input. We anticipate the tools will improve patient access to high-quality IMRT treatment by simplifying the planning process and will reduce the effort and cost of incorporating more advanced planning into clinical practice. Ó 2011 Elsevier Inc.

This paper proposes a new method to solve certain classes of systems of multivariate equations over the binary field and its cryptanalytical applications. We show how heuristic optimization methods such as hill climbing algorithms can be relevant to solving systems of multivariate equations. A characteristic of equation systems that may be efficiently solvable by the means of such algorithms is provided. As an example, we investigate equation systems induced by the problem of recovering the internal state of the stream cipher Trivium. We propose an improved variant of the simulated annealing method that seems to be well-suited for this type of system and provide some experimental results.

Particle Swarm Optimization (PSO) is a heuristic optimization technique showing relationship with Evolutionary Algorithms and strongly based on the concept of swarm. It is used in this paper to face the problem of classification of instances in multiclass databases. Only a few papers exist in literature in which PSO is tested on this problem and there are no papers showing a thorough comparison for it against a wide set of techniques typically used in the field. Therefore in this paper PSO performance is compared on nine typical test databases against those of nine classification techniques widely used for classification purposes. PSO is used to find the optimal positions of class centroids in the database attribute space, via the examples contained in the training set. Performance of a run, instead, is computed as the percentage of instances of testing set which are incorrectly classified by the best individual achieved in the run. Results show the effectiveness of PSO, which turns out to be the best on three out of the nine challenged problems.

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

This paper introduces a generic toolbox of Particle Swarm Optimization developed in the platform of Scilab (PSOTS), with friendly designed interface by TCL/TK. It is developed for a variety of complex problems, including single objective optimization problems, multi-objective optimization problems, continuous problems, discrete problems, and mixed integer problems. The toolbox PSOTS is suitable in the context of non-convex optimization and does not rely on the computation of the derivatives of the objective functions. PSOTS can be also linked with other simulation programs. PSOTS is platform independent, as it is developed in Scilab. It can be distributed and modified freely and can be extended easily. PSOTS provides many technical configurations. Besides, real-time display of convergence curve and optimal solutions are optional according to users' requirements. The particle distribution in the searching space can be demonstrated. Compared with the existing heuristic optimization algorithms in the latest version of Scilab, the convergence accuracy, the convergence rate and the time consumed by using PSOTS are all outstanding. __________________________________________ 978-1-4244-4453-3/09/$25.00 ©2009 IEEE

This paper deals with the economic optimization of reinforced concrete box frames used in road construction. It shows the efficiency of four heuristic algorithms applied to a problem of 50 design variables. Heuristic methods used are the random walk and the descent local search. The metaheuristic methods are the threshold accepting and the simulated annealing. The four methods have been applied to the same frame of 13 m of horizontal span. The comparison of the four heuristic algorithms leads to the conclusion that the proposed threshold accepting is more efficient, since it improves cost results of the random walk and descent local search by 7.5% and 1.4%, respectively, while improving deviation of random results of the simulated annealing. Finally, the inclusion of the deflections and fatigue limit states appears to be crucial, since their ignorance leads to 3.9% more economic but unsafe results.

Parameter estimation procedures are very important in the chemical engineering field for development of mathematical models, since design, optimization and advanced control of chemical processes depend on model parameter values obtained from experimental data. Model nonlinearity makes the estimation of parameter and the statistical analysis of parameter estimates more difficult and more challenging. In this work, it is shown that many of these difficulties can be overcome with the use of heuristic optimization methods, such as the particle swarm optimization (PSO) method. Parameter estimation problems are solved here with PSO and it is shown that the PSO method is efficient for both minimization and construction of the confidence region of parameter estimates. Moreover, it is shown that the elliptical approximation of confidence regions of nonlinear model parameters can be very poor sometimes and that more accurate likelihood confidence regions can be constructed with PSO, allowing for more reliable statistical analysis of the significance of parameter estimates. ᭧

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

Design of a rotor-bearing system is a challenging task due to various conflicting design requirements, which should be fulfilled. This study considers an automatic optimization approach for the design of a rotor supported on tilting-pad bearings. A numerical example of a rotor-bearing system is employed to demonstrate the merits of the proposed design approach. The finite element method is used to model the rotor-bearing system, and the dynamic speed-dependent coefficients of the bearing are calculated using a bulk flow code. A number of geometrical characteristics of the rotor simultaneously with the parameters defining the configuration of tilting pad bearings are considered as design variables into the automatic optimization process. The power loss in bearings, stability criteria, and unbalance responses are defined as a set of objective functions and constraints. The complex design optimization problem is solved using heuristic optimization algorithms, such as genetic, and particle-swarm optimization. Whereas both algorithms found better design solutions than the initial design, the genetic algorithms exhibited the fastest convergence. A statistical approach was used to identify the influence of the design variables on the objective function and constraint measures. The bearing clearances, preloads and lengths showed to have the highest influence on the power loss in the chosen design space. The high performance of the best solution obtained in the optimization design suggests that the proposed approach has good potential for improving design of rotor-bearing systems encountered in industrial applications.

Meta-heuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the one main algorithm is a well known meta-heuristic; Particle Swarm Optimization (PSO). PSO, in its present form, has been in existence for roughly a decade, a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridization and specialization, and demonstration of some interesting emergent behavior. This study comprises a snapshot of particle swarm optimization from the authors' perspective, including variations in the algorithm, modifications and refinements introduced to prevent swarm stagnation and hybridization of PSO with other heuristic algorithms.

This paper presents an extension of the DFAR mathematical optimization model, which unifies the fragmentation, allocation and dynamical migration of data in distributed database systems. The extension consists of the addition of a constraint that models the storage capacity of network sites. This aspect is particularly important in large databases, which exceed the capacity of one or more sites. The Threshold Accepting Algorithm is a variation of the heuristic method known as Simulated Annealing, and it is used for solving the DFAR model. The paper includes experimental results obtained for large test cases.

Launch vehicle design is a complex, multidisciplinary engineering activity that requires making difficult compromises to achieve a balance among competing objectives for the vehicle, including safety, reliability, performance, operability, and cost. Significant work has been done in recent years to advance the design, analysis and optimization of launch vehicles. In the present research effort we propose the application of Particle Swarm Optimization (PSO) in devising a Multidisciplinary Design and Optimization (MDO) strategy for designing a multistage Air-launched Satellite Launch Vehicle (ASLV) with a solid fueled propulsion system. The detailed modules for propulsion characteristics, aerodynamics, mass properties and flight dynamics have been integrated to produce a high fidelity multidisciplinary model of the entire vehicle. Design and optimization of an ASLV is a challenging undertaking and differs from conventional ground launched SLVs. A major difference is in the launch phase, that is, it has to be carried on a mother aircraft to a certain altitude and then launched in a horizontal direction (Flight Path Angle (FPA) = 0 deg). Another difference is in the aerodynamics, that is, ASLV has aerodynamic lifting surfaces in order to improve stability and to provide lift during both the launch phase and within atmospheric flight. A lack of availability of literature on MDO for an ASLV makes our problem even more complex and difficult. PSO is a relatively recent heuristic search method whose mechanics are inspired by swarming or collaborative behavior of biological populations. Simplicity of coding and relatively less computational cost makes PSO a very attractive choice for our problem. PSO has been used as a global optimizer to achieve an optimal solution for attaining a minimum Gross Launch Weight (GLW) while remaining within the set constraints and ensuring delivery of the payload to the desired orbit. The objective of this paper is to develop a design strategy based on PSO that proves to be effective (finding the true global optimal solution) with much better computational efficiency (least computational time) and facilitates system design and optimization of ASLV at the preliminary design level. Use of PSO in system design and optimization of the ASLV makes the present research innovative. The design approach is meant for initial design sizing purposes with minimum basic vehicle data but gives a quick insight on the vehicle performance prior to detailed design.

The Semi-Lagrangian Relaxation (SLR) method has been introduced in [BTV06] to solve the p-median problem. In this paper we apply the method to the Uncapacitated Facility Location (UFL) problem. We perform computational experiments on two main collections of UFL problems with unknown optimal values. On one collection, we manage to solve to optimality 16 out of the 18 instances. On the second collection we solve 2 out of 18 instances and improve the Lagrangian lower bound. In this way, we prove that the Hybrid Multistart heuristic of [RW06] provides near optimal solutions.

The dynamically dimensioned search (DDS) continuous global optimization algorithm by Tolson and Shoemaker (2007) is modified to solve discrete, single-objective, constrained water distribution system (WDS) design problems. The new global optimization algorithm for WDS optimization is called hybrid discrete dynamically dimensioned search (HD-DDS) and combines two local search heuristics with a discrete DDS search strategy adapted from the continuous DDS algorithm. The main advantage of the HD-DDS algorithm compared with other heuristic global optimization algorithms, such as genetic and ant colony algorithms, is that its searching capability (i.e., the ability to find near globally optimal solutions) is as good, if not better, while being significantly more computationally efficient. The algorithm's computational efficiency is due to a number of factors, including the fact that it is not a population-based algorithm and only requires computationally expensive hydraulic simulations to be conducted for a fraction of the solutions evaluated. This paper introduces and evaluates the algorithm by comparing its performance with that of three other algorithms (specific versions of the genetic algorithm, ant colony optimization, and particle swarm optimization) on four WDS case studies (21- to 454-dimensional optimization problems) on which these algorithms have been found to perform well. The results obtained indicate that the HD-DDS algorithm outperforms the state-of-the-art existing algorithms in terms of searching ability and computational efficiency. In addition, the algorithm is easier to use, as it does not require any parameter tuning and automatically adjusts its search to find good solutions given the available computational budget.

Multidimensional item response theory (MIRT) computerized adaptive testing, building on recent work by , is applied in a licensing/certification context. An example of a medical licensure test is used to demonstrate situations in which complex, integrated content must be balanced at the total test level for va-

Meta-heuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the one main algorithm is a well known meta-heuristic; Particle Swarm Optimization (PSO). PSO, in its present form, has been in existence for roughly a decade, a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridization and specialization, and demonstration of some interesting emergent behavior. This study comprises a snapshot of particle swarm optimization from the authors' perspective, including variations in the algorithm, modifications and refinements introduced to prevent swarm stagnation and hybridization of PSO with other heuristic algorithms.

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for Thyristor Controlled Series Compensator (TCSC)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a TCSC-based controller, to enhance power system stability.

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

Parameter estimation procedures are very important in the chemical engineering field for development of mathematical models, since design, optimization and advanced control of chemical processes depend on model parameter values obtained from experimental data. Model nonlinearity makes the estimation of parameter and the statistical analysis of parameter estimates more difficult and more challenging. In this work, it is shown that many of these difficulties can be overcome with the use of heuristic optimization methods, such as the particle swarm optimization (PSO) method. Parameter estimation problems are solved here with PSO and it is shown that the PSO method is efficient for both minimization and construction of the confidence region of parameter estimates. Moreover, it is shown that the elliptical approximation of confidence regions of nonlinear model parameters can be very poor sometimes and that more accurate likelihood confidence regions can be constructed with PSO, allowing for more reliable statistical analysis of the significance of parameter estimates. ᭧

Usually power energy demand increases randomly with time. To enhance system performance, expansion-planning to adapt the power system capacity to the demand is predicted. This paper uses a harmony search metaheuristic optimization method to solve the multi-stage expansion problem for multi-state series-parallel power systems. The study horizon is divided into several periods. At each period the demand distribution is forecasted in the form of a cumulative demand curve. A multiple-choice of additional components from a list of available products can be chosen and included into any subsystem component at any stage to improve the system performance. The components are characterized by their cost, performance (capacity), and availability. The objective is to minimize each investment over its study period while satisfying availability or performance constraints. A universal generating function technique is applied to evaluate power system availability. The harmony search approach is required to identify the optimal combination of adding components with different parameters to be allocated in parallel at each stage.

Meta-heuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the one main algorithm is a well known meta-heuristic; Particle Swarm Optimization (PSO). PSO, in its present form, has been in existence for roughly a decade, a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridization and specialization, and demonstration of some interesting emergent behavior. This study comprises a snapshot of particle swarm optimization from the authors' perspective, including variations in the algorithm, modifications and refinements introduced to prevent swarm stagnation and hybridization of PSO with other heuristic algorithms.

The Griewank function is commonly used to test the ability of different solution procedures to find local optima. It is important to know the exact number of minima of the function to support its use as a test function. However, to the best of our knowledge, no attempts have been made to analytically derive the number of minima. Because of the complex nature of the function surface, a numerical method is developed to restrict domain spaces to hyperrectangles satisfying certain conditions. Within these domain spaces, an analytical method to count the number of minima is derived and proposed as a recursive functional form. The numbers of minima for two search spaces are provided as a reference.

This paper presents an extension of the DFAR mathematical optimization model, which unifies the fragmentation, allocation and dynamical migration of data in distributed database systems. The extension consists of the addition of a constraint that models the storage capacity of network sites. This aspect is particularly important in large databases, which exceed the capacity of one or more sites. The Threshold Accepting Algorithm is a variation of the heuristic method known as Simulated Annealing, and it is used for solving the DFAR ...

The linear arrangement minimization problem consists of finding a labeling or arrangement of the vertices of a graph that minimizes the sum of the absolute values of the differences between the labels of adjacent vertices. This is a well-known NP-hard problem that presents a challenge to solution methods based on heuristic optimization. Many linear arrangement reduction algorithms have recently been developed and applied to structural engineering, VLSI and software testing. We undertake the development of different heuristic procedures with the goal of uncovering the most effective designs to tackle this difficult but important problem. Specifically, we consider the adaptation of constructive, local search, GRASP and Path Relinking methods for the linear arrangement minimization. We perform computational experiments with previously reported instances to first study the effects of changes in critical search parameters and then to compare the efficiency of our proposal with previous s...

The selection of a set of requirements between all those previously defined by the customers is a very important process in software development, that can be addressed using meta-heuristic optimization techniques. Dependencies or interactions between requirements can be defined to denote common situations in software development: requirement that follow an order of precedence, requiments exclusive of each other, requirements that must be included at the same time, etc. These interactions add new contraints to the requirement selection problem leading to an adaptation of the search techniques. This paper studies how requirement interactions affect the search space explored by optimization algorithms and how to adapt three search techniques, i.e. a greedy randomized adaptive search procedure (GRASP), a genetic algorithm (GA) and an ant colony system (ACS), for the requirements selection problem which includes interaction between requirements. The problem of requirement selection, including requirements interactions, is formally introduced. Then, we describe the adaptation of the three meta-heuristic algorithms to solve this problem. And finally, we compare the performance of the meta-heuristic approaches applied by means of computational experiments conducted on two different instances of the problem, constructed from data provided by the experts.

Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization (MDO) process. The complexity of problem demands highly efficient and effective algorithm that can optimize the design. Hyper heuristic approach (HHA) based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle (ASLV). A non-learning random function (NLRF) is proposed to control low-level meta-heuristics (LLMHs) that increases certainty of global solution, an essential ingredient required in product conceptual design phase of aerospace systems. Comprehensive empirical study is performed to evaluate the performance advantages of proposed approach over popular non-gradient based optimization methods. Design of ASLV encompasses aerodynamics, propulsion, structure, stages layout, mass distribution, and trajectory modules connected by multidisciplinary feasible design approach. This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer. This distinctive approach for launch vehicle system design relieves engineers from tedious, iterative task and enables them to improve their component level models. Mass is an impetus on vehicle performance and cost, and so it is considered as the core of vehicle design process. Therefore, gross launch mass is to be minimized in HHA.

Abstract:Locating an economic facility (eg plant, warehouse or retail store) is one of the most strategic and important issue that a business company faces. This planning question is in the operations research literature referred as a facility location problem. It focuses on defining ...

ngineers and scientists use instrumentation and measurement equipment to obtain information for specific environments, such as temperature and pressure. This task can be performed manually using portable gauges. However, there are many instances in which this approach may be impractical; when gathering data from remote sites or from potentially hostile environments. In these applications, autonomous navigation methods allow a mobile robot to explore an environment independent of human presence or intervention. The mobile robot contains the measurement

Evolution strategies are heuristic optimization techniques which emulate biological systems. In this paper first it is shown that they can be formulated as one-dimensional maps and that they can be extremely sensitive to initial conditions. Then it is shown how this chaotic behavior can be exploited to efficiently conduct search operations for a globally optimal solution.

Maritime container terminals are facilities where cargo containers are transshipped between ships or between ships and land vehicles (trucks or trains). These terminals involve a large number of complex and combinatorial problems. Two important problems are the Container Stacking Problem and the Berth Allocation Problem. Both problems are generally managed and solved independently but there exist a relationship that must be taken into account to optimize the whole process. The terminal operator normally demands all containers bound for an incoming vessel to be ready in the terminal before its arrival. Similarly, customers (i.e., vessel owners) expect prompt berthing of their vessels upon arrival. This is particularly important for vessels from priority customers who may have been guaranteed berth-on-arrival service in their contract with the terminal operator. To this end, both problems must be interrelated.

Since the publication of the seed paper of Simulated Annealing algorithm (SA) written by Kirkpatrich, several methods have been proposed to get the cooling scheme parameters. Although developed for SA, some of these methods can be extended to the algorithm known as Threshold Accepting (TA). SA and TA are quite similar and both are treated in this paper as a Simulated Annealing Like (SAL) algorithm. This paper presents a method to set the cooling scheme parameters in SAL algorithms; it establishes that both, the initial and the final temperatures are function of the maximum and minimum cost increment getting from the neighborhood structure. Experimentation with Traveling Salesman Problem and Hydraulic Network Design Problem shows that the cooling schemes getting through our method are more efficient than the previous ones.

Sampled fictitious play (SFP) is a recently proposed iterative learning mechanism for computing Nash equilibria of non-cooperative games. For games of identical interests, every limit point of the sequence of mixed strategies induced by the empirical frequencies of best response actions that players in SFP play is a Nash equilibrium. Because discrete optimization problems can be viewed as games of identical interests wherein Nash equilibria define a type of local optimum, SFP has recently been employed as a heuristic optimization algorithm with promising empirical performance. However, there have been no guarantees of convergence to a globally optimal Nash equilibrium established for any of the problem classes considered to date. In this paper, we introduce a variant of SFP and show that it converges almost surely to optimal policies in model-free, finite-horizon stochastic dynamic programs. The key idea is to view the dynamic programming states as players, whose common interest is to maximize the total multi-period expected reward starting in a fixed initial state. We also offer empirical results suggesting that our SFP variant is effective in practice for small to moderate sized model-free problems.

Multidimensional item response theory (MIRT) computerized adaptive testing, building on recent work by , is applied in a licensing/certification context. An example of a medical licensure test is used to demonstrate situations in which complex, integrated content must be balanced at the total test level for va-

The goal for real-time mobile robots is to travel the shortest path in minimal time while avoiding obstacles in a navigation environment. Autonomous navigation allows robots to plan this path without the need for human intervention. The path-planning problem has been shown to be NP-hard, thus this problem is often solved using heuristic optimization methods such as genetic algorithms. An important part of the genetic algorithm solution is the structure of the genotype that represents paths in the navigation environment. The genotype must represent a valid path, but still be simple to process by the genetic algorithm in order to reduce computational requirements. Unfortunately, many contemporary genetic path-planning algorithms use complex structures that require a significant amount of processing, which can affect the real-time response of the robot. This paper describes the development of a genotype structure that contains only the essential information for path planning, which all...