Abstract
Particle swarm optimization (PSO) has been widely used in multi-objective engineering design optimization where parameter selection is of prime importance. This paper proposes a multi-objective particle swarm optimizer (MOPSO) with a modified crowding factor and enhanced local search ability. A new parameter-less sharing method is introduced to estimate the density of particles’ neighborhood in the search space. Initially, the proposed method determines the crowding factor of the solutions; in later stages, it effectively guides the entire swarm to converge closely to the true Pareto front. In addition, the gradient descent search method is applied. The algorithm’s performance on two engineering design problems is highlighted and compared with other approaches. The obtained results demonstrate that the proposed algorithm is capable of effectively searching along the Pareto optimal front and successfully obtaining trade-off solutions for the engineering design problems.
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Ma, L., Forouraghi, B. (2012). A Modified Particle Swarm Optimizer for Engineering Design. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_20
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DOI: https://doi.org/10.1007/978-3-642-31087-4_20
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