College of Mathematics and Computer Science Guangxi University for Nationalities, Nanning 530006, China

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Abstract—This paper presents an effective hybrid coevolutionary particle swarm optimization algorithm for solving constrained engineering design problems, which is based on simulated annealing (SA) , employing the notion of co-evolution to adapt penalty factors. By employing the SAbased selection for the best position of particles and swarms when updating the velocity in co-evolutionary particle swarm optimization algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed, and can reach a high precision.

Index Terms—Particle swarm optimization; Simulated annealing; Constrained optimization; Co-evolution; Penalty function

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Cite: Yongquan Zhou and Shengyu Pei, " A Hybrid Co-evolutionary Particle Swarm Optimization Algorithm for Solving Constrained Engineering Design Problems," Journal of Computers vol. 5, no. 6, pp. 965-972, 2010.