Hybrid directional-biased evolutionary algorithm for multi-objective optimization

Authors:
Tomohiro Shimada;Masayuki Otani;Hiroyasu Matsushima;Hiroyuki Sato;Kiyohiko Hattori;Keiki Takadama
Affiliations:
The University of Electro-Communication, Tokyo, Japan;The University of Electro-Communication, Tokyo, Japan;The University of Electro-Communication, Tokyo, Japan;The University of Electro-Communication, Tokyo, Japan;The University of Electro-Communication, Tokyo, Japan;The University of Electro-Communication, Tokyo, Japan
Venue:
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Year:
2010

Citing 2
Cited 0

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PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature

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Abstract

This paper proposes the hybrid Indicator-based Directionalbiased Evolutionary Algorithm (hIDEA) and verifies its effectiveness through the simulations of the multi-objective 0/1 knapsack problem. Although the conventional Multi-objective Optimization Evolutionary Algorithms (MOEAs) regard the weights of all objective functions as equally, hIDEA biases the weights of the objective functions in order to search not only the center of true Pareto optimal solutions but also near the edges of them. Intensive simulations have revealed that hIDEA is able to search the Pareto optimal solutions widely and accurately including the edge of true ones in comparison with the conventional methods.