Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Artificial bee colony algorithm with multiple onlookers for constrained optimization problems
ECC'11 Proceedings of the 5th European conference on European computing conference
Guided artificial bee colony algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
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Constraint handling is one of the most difficult parts encountered in practical engineering design optimizations. Different kinds of methods were proposed for handling constraints namely, genetic algorithm, self-adaptive penalty approach and other evolutionary algorithms. Particle Swarm Optimization (PSO) efficiently solved most nonlinear optimization problems with inequity constraints. This study hybridizes PSO with a meta-heuristic algorithm called Tabu Search (TS) to solve the same engineering design problems. The algorithm starts with a population of particles or solution generated randomly and is updated using the update equations of PSO. The updated particles are then subjected to Tabu Search for further refinement. The PSO algorithm handles the global search for the solution while TS facilitates the local search. With embedded hyrbridization, this study which we call PSO-TS, showed better results compared to algorithms reported in Hu et al's study as applied to four benchmark engineering problems. Specifically, this study beat the results of Coello, Deb and Hu.