Comparison of Multiobjective Memetic Algorithms on 0/1 Knapsack Problems

This paper compares two well-known multiobjective memetic algorithms through computational experiments on 0/1 knapsack problems. The two algorithms are MOGLS (multiple objective genetic local search) of Jaszkiewicz and M-PAES (memetic Pareto archived evolution strategy) of Knowles & Corne. It is shown that the MOGLS with a sophisticated repair algorithm based on the current weight vector in the scalar fitness function has much higher search ability than the M-PAES with a simple repair algorithm. When they use the same simple repair algorithm, the M-PAES performs better overall. It is also shown that the diversity of non-dominated solutions obtained by the MPAES is small in comparison with the MOGLS. For improving the performance of the M-PAES, we examine the use of the scalar fitness function with a random weight vector in the selection procedure of parent solutions.

[1]  Andrzej Jaszkiewicz.  Comparison of local search-based metaheuristics on the multiple objective knapsack problem , 2001 .

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

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

[4]  D. Corne,et al.  On Metrics for Comparing Non Dominated Sets , 2001 .

[5]  Andrzej Jaszkiewicz,et al.  On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment , 2002, IEEE Trans. Evol. Comput..

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

[7]  Andrzej Jaszkiewicz,et al.  Genetic local search for multi-objective combinatorial optimization , 2022 .

[8]  Hisao Ishibuchi,et al.  Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling , 2003, IEEE Trans. Evol. Comput..

[9]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[10]  Hisao Ishibuchi,et al.  A multi-objective genetic local search algorithm and its application to flowshop scheduling , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[11]  Joshua D. Knowles,et al.  M-PAES: a memetic algorithm for multiobjective optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[12]  David Corne,et al.  A comparison of diverse approaches to memetic multiobjective combinatorial optimization , 2000 .

[13]  Joshua D. Knowles,et al.  On metrics for comparing nondominated sets , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[15]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..