Effects of δ-Similar Elimination and Controlled Elitism in the NSGA-II Multiobjective Evolutionary Algorithm

In this paper, we propose <S-similar elimination to induce a better distribution of non-dominated solutions and distribute more fairly selection pressure among them in order to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. With the proposed method similar individuals are eliminated in the process of evolution by using the distance between individuals in objective space. We investigate four eliminating methods to verify the effects of J-similar elimination and compare the search performance of enhanced NSGA-II by our method and by controlled elitism, which emphasizes the inclusion of lateral diversity.

[1]  Prabhat Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[2]  Tomoyuki Hiroyasu,et al.  NCGA: Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems , 2002, GECCO Late Breaking Papers.

[3]  Hisao Ishibuchi,et al.  Mating Scheme for Controlling the Diversity-Convergence Balance for Multiobjective Optimization , 2004, GECCO.

[4]  T. Hiroyasu,et al.  NEIGHBORHOOD CULTIVATION GENETIC ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS , 2002 .

[5]  Hisao Ishibuchi,et al.  Effects of Removing Overlapping Solutions on the Performance of the NSGA-II Algorithm , 2005, EMO.

[6]  Kalyanmoy Deb,et al.  Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence , 2001, EMO.

[7]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[8]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[9]  Piotr Czyzżak,et al.  Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization , 1998 .

[10]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[11]  Tomoyuki Hiroyasu,et al.  SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2 , 2004, PPSN.

[12]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[13]  Kiyoshi Tanaka,et al.  Selection, Drift, Recombination, and Mutation in Multiobjective Evolutionary Algorithms on Scalable MNK-Landscapes , 2005, EMO.

[14]  Hisao Ishibuchi,et al.  A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization , 2003, GECCO.

[15]  Hisao Ishibuchi,et al.  An Empirical Study on the Effect of Mating Restriction on the Search Ability of EMO Algorithms , 2003, EMO.