List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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Evolutionary algorithms for solving multi-objective problems

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.

Handling multiple objectives with particle swarm optimization

https://www.control.isy.liu.se/student/graduate/hybrid_course/oh_BemMor.pdf

Control of systems integrating logic, dynamics, and constraints

I What Are the Ages of My Three Sons?.- 1 Why Are Some Problems Difficult to Solve?.- II How Important Is a Model?.- 2 Basic Concepts.- III What Are the Prices in 7-11?.- 3 Traditional Methods - Part 1.- IV What Are the Numbers?.- 4 Traditional Methods - Part 2.- V What's the Color of the Bear?.- 5 Escaping Local Optima.- VI How Good Is Your Intuition?.- 6 An Evolutionary Approach.- VII One of These Things Is Not Like the Others.- 7 Designing Evolutionary Algorithms.- VIII What Is the Shortest Way?.- 8 The Traveling Salesman Problem.- IX Who Owns the Zebra?.- 9 Constraint-Handling Techniques.- X Can You Tune to the Problem?.- 10 Tuning the Algorithm to the Problem.- XI Can You Mate in Two Moves?.- 11 Time-Varying Environments and Noise.- XII Day of the Week of January 1st.- 12 Neural Networks.- XIII What Was the Length of the Rope?.- 13 Fuzzy Systems.- XIV Everything Depends on Something Else.- 14 Coevolutionary Systems.- XV Who's Taller?.- 15 Multicriteria Decision-Making.- XVI Do You Like Simple Solutions?.- 16 Hybrid Systems.- 17 Summary.- Appendix A: Probability and Statistics.- A.1 Basic concepts of probability.- A.2 Random variables.- A.2.1 Discrete random variables.- A.2.2 Continuous random variables.- A.3 Descriptive statistics of random variables.- A.4 Limit theorems and inequalities.- A.5 Adding random variables.- A.6 Generating random numbers on a computer.- A.7 Estimation.- A.8 Statistical hypothesis testing.- A.9 Linear regression.- A.10 Summary.- Appendix B: Problems and Projects.- B.1 Trying some practical problems.- B.2 Reporting computational experiments with heuristic methods.- References.

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How to Solve It: Modern Heuristics

Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.

/pdf/evolutionary-optimization-in-uncertain-environments-a-survey-1cgcnwsuec.pdf

Evolutionary optimization in uncertain environments-a survey

Paper: A method for auto-tuning of PID control parameters

Recently, multi-objective optimization by use of the genetic algorithms (GAs) is getting a growing interest as a novel approach to this problem. Population based search of GA is expected to find the Pareto optimal solutions of the multi-objective optimization problems in parallel. To achieve this goal, it is an intrinsic requirement that the evolution process of GA maintains well the diversity of the population in the Pareto optimality set. In this paper, the authors propose to utilize the Thermodynamical Genetic Algorithm (TDGA), a genetic algorithm that uses the concepts of the entropy and the temperature in the selection operation, for multi-objective optimization. Combined with the Pareto-based ranking technique, the computer simulation shows that TDGA can find a variety of Pareto optimal solutions.

Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm

Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm

Niching enables a genetic algorithm (GA) to maintain diversity in a population. It is particularly useful when the problem has multiple optima where the aim is to find all or as many as possible of these optima. When the fitness landscape of a problem changes overtime, the problem is called non–stationary, dynamic or time–variant problem. In these problems, niching can maintain useful solutions to respond quickly, reliably and accurately to a change in the environment. In this paper, we present a niching method that works on the problem substructures rather than the whole solution, therefore it has less space complexity than previously known niching mechanisms. We show that the method is responding accurately when environmental changes occur.

Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm.

For successful applications of the genetic algorithm, there are two important points to be considered. The first point is the design of the fitness landscape introduced by the representation of the solution as a gene and searching operations such as crossover and mutation. The second is control of the convergence brought about by the selection operation. In the conventional implementation of GA, these two points are mutually dependent, i.e., a suitable selection pressure varies largely depending on, e.g., the crossover operator. Hence, it requires much trial-and-error effort to find a nice configuration of GA. The authors apply a novel selection rule, the Thermodynamical Genetic Algorithm (TDGA) proposed by N. Mori et al. (1995) to the traveling salesman problem (TSP), and propose an adaptive annealing schedule of the temperature in TDGA. Computer simulation with several crossover operators for TSP shows that TDGA reduces the mutual dependency between the fitness landscape and the convergence process.

Yoshikazu Nishikawa

Papers

Paper: A method for auto-tuning of PID control parameters

Multi-Objective Optimization by Means of the Thermodynamical Genetic Algorithm

Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm

Adaptation to Changing Environments by Means of the Memory Based Thermodynamical Genetic Algorithm.

A genetic solution for the traveling salesman problem by means of a thermodynamical selection rule