Abstract
This paper proposes a PSO-based hybrid multi-objective algorithm (HMOPSO) with the following three main features. First, the HMOPSO takes the crossover operator of the genetic algorithm as the particle updating strategy. Second, a propagating mechanism is adopted to propagate the non-dominated archive. Third, a local search heuristic based on scatter search is applied to improve the non-dominated solutions. Computational study shows that the HMOPSO is competitive with previous multi-objective algorithms in literature.
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Wang, X., Tang, L. (2011). A PSO-Based Hybrid Multi-Objective Algorithm for Multi-Objective Optimization Problems. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_4
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DOI: https://doi.org/10.1007/978-3-642-21524-7_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21523-0
Online ISBN: 978-3-642-21524-7
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