Bare-Bones Multiobjective Particle Swarm Optimization Based on Parallel Cell Balanceable Fitness Estimation
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TL;DR: Experimental results show that the proposed NBBMOPSO outperforms all the other methods in terms of the chosen performance metrics.
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Abstract: The convergence and diversity of the Pareto optimal solutions is of great importance for multiobjective evolutionary algorithms. Based on parallel cell balanceable fitness estimation (PCBFE), a novel bare-bones multiobjective particle swarm optimization (NBBMOPSO) algorithm is proposed in this paper. First, the PCBFE strategy, which is based on the parallel cell mapping approach, is developed to retain the balance between the proximity and the diversity. After that, the PCBFE strategy is adopted to maintain external archive and update leaders. Second, an adaptive update strategy for crossover probability is designed to repair the weakness of particle search. Finally, an elitism learning strategy is performed to exchange useful information among solutions in the external archive, which can enhance the capability of dropping out of the local Pareto front. To demonstrate the merits of NBBMOPSO for multiobjective optimization, Zitzler-Deb-Thiele (ZDT) and Deb-Thiele-Laumanns-Zitzler (DTLZ) test suits are examined with comparisons against the other seven state-of-the-art competitors. Experimental results show that the proposed NBBMOPSO outperforms all the other methods in terms of the chosen performance metrics.
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References
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Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects
Yong Zhang,Dunwei Gong,Na Geng,Xiaoyan Sun +3 more
- 01 May 2014
TL;DR: An efficient hybrid particle swarm optimization algorithm to solve dynamic economic dispatch problems with valve-point effects is presented, by integrating an improved bare-bones particle Swarm optimization (BBPSO) with a local searcher called directionally chaotic search (DCS).
A Study of Collapse in Bare Bones Particle Swarm Optimization
TL;DR: It is conjectured that, subject to spread, stability and no-collapse, there is a single encompassing particle swarm paradigm, and that an important aspect of parameter tuning within any particular manifestation is to remove any deleterious behavior that ensues from the dynamics.