Dynamic population size in multiobjective evolutionary algorithms

Authors:
Haiming Lu;G. G. Yen
Affiliations:
Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA;Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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Abstract

The authors propose a new evolutionary approach to multiobjective optimization problems; the Dynamic Multiobjective Evolutionary Algorithm (DMOEA). In DMOEA, a population growing and population decline strategies are designed, and several important indicators are defined in order to determine the adaptive individual "killing" scheme. By examining the selected performance indicators of a test function, DMOEA is found to be effective in directing the population into an optimal population size, keeping the diversity of the individuals along the trade-off surface, tending to extend the Pareto front to new areas, and finding a well-approximated Pareto optimal front.