A Hybrid Co-evolutionary Particle Swarm Optimization Algorithm for Solving Constrained Engineering Design Problems

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.