An effective co-evolutionary particle swarm optimization for constrained engineering design problems |
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Affiliation: | 1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;2. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt;3. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI Av. Revolucion 1500, Guadalajara, Jal, Mexico;4. Cybernetics Institute, Tomsk Polytechnic University, Lenin Avenue 30, Tomsk, Russian Federation;5. Hubei Collaborative Innovation Center of Basic Education Information Technology Services, Hubei University of Education, Wuhan, China;1. School of Information and Communication Technology, Griffith University, Nathan Campus, Brisbane, QLD 4111, Australia;2. Griffith College, Mt Gravatt, Brisbane, QLD 4122, Australia |
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Abstract: | Many engineering design problems can be formulated as constrained optimization problems. So far, penalty function methods have been the most popular methods for constrained optimization due to their simplicity and easy implementation. However, it is often not easy to set suitable penalty factors or to design adaptive mechanism. By employing the notion of co-evolution to adapt penalty factors, this paper proposes a co-evolutionary particle swarm optimization approach (CPSO) for 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. The proposed CPSO is population based and easy to implement in parallel. Especially, penalty factors also evolve using PSO in a self-tuning way. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method. Moreover, the CPSO obtains some solutions better than those previously reported in the literature. |
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