Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs. These methods are intended for solving real-world application problems that have many constraints and very small feasible regions. One method called Objective Exchange Genetic Algorithm for Design Optimization (OEGADO) runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with the others. The other method called Objective Switching Genetic Algorithm for Design Optimization (OSGADO) runs each objective sequentially with a common population for all objectives. Empirical results in benchmark and engineering design domains are presented. A comparison between our methods and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows that our methods performed better than NSGA-II for difficult problems and found Pareto-optimal solutions in fewer objective evaluations. The results suggest that our methods are better applicable for solving real-world application problems wherein the objective computation time is large.

[1]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[2]  Haym Hirsh,et al.  Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models , 2000, GECCO.

[3]  A. Osyczka,et al.  A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm , 1995 .

[4]  Khaled Rasheed,et al.  Comparison Of Methods For Using Reduced Models To Speed Up Design Optimization , 2002, GECCO.

[5]  T. T. Binh.  MOBES : A multiobjective evolution strategy for constrained optimization problems , 1997 .

[6]  K. Rasheed,et al.  An incremental-approximate-clustering approach for developing dynamic reduced models for design optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[7]  Kalyanmoy Deb,et al.  Mechanical Component Design for Multiple Objectives Using Elitist Non-dominated Sorting GA , 2000, PPSN.

[8]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[9]  Haym Hirsh,et al.  Learning to be selective in genetic-algorithm-based design optimization , 1999, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[10]  Haym Hirsh,et al.  Gado: a genetic algorithm for continuous design optimization , 1998 .

[11]  Khaled Rasheed,et al.  Comparison of methods for developing dynamic reduced models for design optimization , 2002, Soft Comput..

[12]  S. Ranji Ranjithan,et al.  Constraint Method-Based Evolutionary Algorithm (CMEA) for Multiobjective Optimization , 2001, EMO.

[13]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[14]  Masahiro Tanaka,et al.  GA-based decision support system for multicriteria optimization , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[15]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[16]  K. Deb,et al.  Design of truss-structures for minimum weight using genetic algorithms , 2001 .

[17]  Kalyanmoy Deb,et al.  MULTI-OBJECTIVE FUNCTION OPTIMIZATION USING NON-DOMINATED SORTING GENETIC ALGORITHMS , 1994 .

[18]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[19]  Marco Laumanns,et al.  Combining Convergence and Diversity in Evolutionary Multiobjective Optimization , 2002, Evolutionary Computation.

[20]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .