A competitive mechanism based multi-objective particle swarm optimizer with fast convergence
By Xing-yi Zhang, Xiutao Zheng, Ran Cheng, Jianfeng Qiu, Yaochu Jin. Information Sciences, 2018
373 citations recorded by the source corpus.
Abstract In the past two decades, multi-objective optimization has attracted increasing interests in the evolutionary computation community, and a variety of multi-objective optimization algorithms have been proposed on the basis of different population based meta-heuristics, where the family of multi-objective particle swarm optimization is among the most representative ones. While the performance of most existing multi-objective particle swarm optimization algorithms largely depends on the global or personal best particles stored in an external archive, in this paper, we propose a competitive mechanism based multi-objective particle swarm optimizer, where the particles are updated on the basis of the pairwise competitions performed in the current swarm at each generation. The performance of the proposed competitive multi-objective particle swarm optimizer is verified by benchmark comparisons with several state-of-the-art multi-objective optimizers, including three multi-objective particle swarm optimization algorithms and three multi-objective evolutionary algorithms. Experimental results demonstrate the promising performance of the proposed algorithm in terms of both optimization quality and convergence speed.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/53ecae5e1006521e739ca584e8702dd2243a6abc
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems (2020, methodology): “1) Algorithms: Four MOEAs are compared with SparseEA on SMOP1–SMOP8, i.e., NSGA-II [23], CMOPSO [63], MOEA/D-DRA [71], and WOF-NSGA-II [31], where NSGA-II is a genetic algorithm, CMOPSO is a particle swarm optimization algorithm, MOEA/D-DRA is a differential evolution algorithm, and WOF-NSGA-II is…”
- Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “Nevertheless, existing multiobjective PSO and CSO algorithms are effective in solving MOPs with no more than 50 decision variables [33], [40].”