Dynamic population size in multiobjective evolutionary algorithms

Author

Lu, Haiming ; Yen, Gary G.

Author_Institution

Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1648

Lastpage

1653

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

Keywords

evolutionary computation; probability; search problems; DMOEA; Dynamic Multiobjective Evolutionary Algorithm; Pareto optimal front; adaptive individual killing scheme; dynamic population size; multiobjective optimization; optimal population size; performance indicators; searching; Computational complexity; Convergence; Distributed computing; Evolutionary computation; Heuristic algorithms; Optimization methods; Perturbation methods; Sampling methods; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1004489

Filename

1004489