Abstract
The convergence of meta-heuristic optimization algorithms is not mathematically ensured given their heuristic nature of mimicking natural phenomena. Nevertheless, in recent years, they have become very widespread tools due to their successful capability to handle hard constrained problems. In the present study, the particle swarm optimization (PSO) algorithm is investigated. The most important state-of-the-art improvements (inertia weight and neighbourhood) have been implemented and an unfeasible local search operator based on self-adaptive Evolutionary Strategy (ES) algorithm has been proposed. Firstly, the current PSO-ES has been tested on literature constrained benchmark numerical problems compared with PSO which adopts the traditional penalty function approach. In conclusion, some constrained structural optimization truss design examples have been covered and critically discussed.
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Rosso, M.M., Aloisio, A., Cucuzza, R., Asso, R., Marano, G.C. (2023). Structural Optimization With the Multistrategy PSO-ES Unfeasible Local Search Operator. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-19-6631-6_16
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