TY - GEN
T1 - Nonlinear constrained optimization by enhanced co-evolutionary PSO
AU - He, Qie
AU - Wang, Ling
AU - Huang, Fu Zhuo
PY - 2008
Y1 - 2008
N2 - Penalty function methods have been the most popular methods for nonlinear constrained optimization due to their simplicity and easy implementation. However, it is often not easy to set suitable penalty factors or to design adaptive mechanisms. By employing the notion of co-evolution to adapt penalty factors, we present a co-evolutionary particle swarm optimization approach (CPSO) for nonlinear constrained optimization problems, where PSO is applied with two kinds of swarms for evolutionary exploration and exploitation in spaces of both solutions and penalty factors. To enhance the performance of our proposed algorithm, three improvement strategies are proposed. The proposed algorithm is population-based and easy to implement in parallel, in which the penalty factors to evolve in a self-tuning way. Simulation results based on three famous engineering constrained optimization problems demonstrate the effectiveness, efficiency and robustness of the proposed enhanced CPSO (ECPSO).
AB - Penalty function methods have been the most popular methods for nonlinear constrained optimization due to their simplicity and easy implementation. However, it is often not easy to set suitable penalty factors or to design adaptive mechanisms. By employing the notion of co-evolution to adapt penalty factors, we present a co-evolutionary particle swarm optimization approach (CPSO) for nonlinear constrained optimization problems, where PSO is applied with two kinds of swarms for evolutionary exploration and exploitation in spaces of both solutions and penalty factors. To enhance the performance of our proposed algorithm, three improvement strategies are proposed. The proposed algorithm is population-based and easy to implement in parallel, in which the penalty factors to evolve in a self-tuning way. Simulation results based on three famous engineering constrained optimization problems demonstrate the effectiveness, efficiency and robustness of the proposed enhanced CPSO (ECPSO).
UR - http://www.scopus.com/inward/record.url?scp=55749105300&partnerID=8YFLogxK
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U2 - 10.1109/CEC.2008.4630780
DO - 10.1109/CEC.2008.4630780
M3 - Conference contribution
AN - SCOPUS:55749105300
SN - 9781424418237
T3 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
SP - 83
EP - 89
BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
T2 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
Y2 - 1 June 2008 through 6 June 2008
ER -