Co-evolutionary particle swarm optimization to solve min-max problems

Author

Shi, Yuhui ; Krohling, Renato A.

Author_Institution

EDS Embedded Syst. Group, Kokomo, IN, USA

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1682

Lastpage

1687

Abstract

A co-evolutionary particle swarm optimization (PSO) to solve constrained optimization problems is proposed. First, we introduce the augmented Lagrangian to transform a constrained optimization to a min-max problem with the saddle-point solution. Next, a co-evolutionary PSO algorithm is developed with one PSO focusing on the minimum part of the min-max problem with the other PSO focusing on the maximum part of the min-max problem. The two PSOs are connected through the fitness function. In the fitness calculation of one PSO, the other PSO serves as the environment to that PSO. The new algorithm is tested on three benchmark functions. The simulation results illustrate the efficiency and effectiveness of the new co-evolutionary particle swarm algorithm

Keywords

artificial life; evolutionary computation; augmented Lagrangian; benchmark functions; co-evolutionary particle swarm optimization; constrained optimization problems; evolutionary algorithms; fitness function; min-max problem solving; saddle-point solution; simulation; Annealing; Benchmark testing; Constraint optimization; Decoding; Evolutionary computation; Genetic algorithms; Lagrangian functions; Particle swarm optimization; Sorting; Sun;

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.2002.1004495

Filename

1004495