Multi-Objective Optimization using Evolutionary AlgorithmsEvolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study. |
From inside the book
Page 495
... Genetic Algorithms and Engineering Design . New York : Wiley . Geoffrion , A. M. , Dyer , J. S. and Feinberg , A. ( 1972 ) . An interactive approach for multi- criterion optimization with an application to the operation of an academic ...
... Genetic Algorithms and Engineering Design . New York : Wiley . Geoffrion , A. M. , Dyer , J. S. and Feinberg , A. ( 1972 ) . An interactive approach for multi- criterion optimization with an application to the operation of an academic ...
Contents
Prologue | 1 |
MultiObjective Optimization | 13 |
2 | 56 |
Classical Methods | 75 |
4 | 115 |
Exercise Problems | 165 |
NonElitist MultiObjective Evolutionary Algorithms | 171 |
50 | 229 |
Exercise Problems | 286 |
Exercise Problems | 314 |
Evolutionary Algorithms | 438 |
Exercise Problems | 441 |
Applications of MultiObjective Evolutionary Algorithms | 447 |
6 | 467 |
Epilogue | 481 |
489 | |
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Common terms and phrases
archive assigned fitness best non-dominated better calculated choose chosen clusters constraint violation convergence corresponding crossover operator crowding distance decision variable space distribution dominated solutions elitist equation Euclidean distance evaluated evolution strategy evolutionary algorithms Evolutionary Computation external population f₁ feasible solutions fmax genetic algorithm genetic operations goal programming hypercube infeasible solutions local search mating pool maximum method metric Minimize f(x minimum MOEA multi-objective evolutionary algorithms multi-objective optimization problem mutation operator niche count non-dominated front non-dominated set non-dominated solutions nonconvex NPGA NSGA NSGA-II number of solutions objective function values objective space offspring population optimal solutions optimum Oshare parent solutions Pareto Pareto-optimal region Pareto-optimal set Pareto-optimal solutions performed population members Pt+1 random real-parameter search space selection operator set of solutions sharing function shown in Figure shows solving SPEA Step strategy string subpopulation suggested technique test problems tournament selection trade-off solutions true Pareto-optimal front VEGA w₁ WBGA weight vector
Popular passages
Page 508 - Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: Empirical results.
Page 489 - T 1996 Evolutionary Algorithms in Theory and Practice (New York: Oxford University Press...
Page 494 - Fogel, LJ., Angeline, PJ and Fogel, DB (1995). An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines.