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
Adaptive Particle Swarm Optimization
Zhi-Hui Zhan,Jun Zhang +1 more
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TL;DR: An adaptive particle swarm optimization with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach is proposed, resulting in substantially improved quality of global solutions.
An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
TL;DR: In this paper, a novel, computationally efficient approach to nondominated sorting is proposed, termed efficient nondominated sort (ENS), where a solution to be assigned to a front needs to be compared only with those that have already been assigned toA front, thereby avoiding many unnecessary dominance comparisons.
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Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems
TL;DR: In this article, a coevolutionary multi-objective evolutionary algorithm named multiple populations for multiple objectives (MPMO) was proposed to solve multiobjective optimization problems.
341
A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch
TL;DR: A new bare-bones multi-objective particle swarm optimization algorithm which has three distinctive features: a particle updating strategy which does not require tuning up control parameters; a mutation operator with action range varying over time to expand the search capability; and an approach based on particle diversity to update the global particle leaders.
300
Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems
Qiuzhen Lin,Songbai Liu,Qingling Zhu,Chaoyu Tang,Ruizhen Song,Jianyong Chen,Carlos A. Coello Coello,Ka-Chun Wong,Jun Zhang +8 more
TL;DR: A balanceable fitness estimation method and a novel velocity update equation are presented, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs.
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