publications

@article{li_large-scale_2022,

title = {A Large-Scale Multiobjective Particle Swarm Optimizer With Enhanced Balance of Convergence and Diversity},

author = {Dongyang Li and Lei Wang and Li Li and Weian Guo and Qidi Wu and Alexander Lerch},

doi = {10.1109/TCYB.2022.3225341},

issn = {2168-2275},

year  = {2022},

date = {2022-01-01},

journal = {IEEE Transactions on Cybernetics},

pages = {1--12},

abstract = {Large-scale multiobjective optimization problems (LSMOPs) continue to be challenging for existing multiobjective evolutionary algorithms (MOEAs). The main difficulties are that: 1) the diversity preservation in both the objective space and the decision space needs to be taken into account when solving LSMOPs and 2) the existing learning structures in current MOEAs usually make the learning operators only coincidentally serve convergence and diversity, leading to difficulties in balancing these two factors. Therefore, balancing convergence and diversity in current MOEAs is difficult. To address these issues, this article proposes a multiobjective particle swarm optimizer with enhanced balance of convergence and diversity (MPSO-EBCD). In MPSO-EBCD, a novel velocity update structure for multiobjective particle swarm optimization is put forward, dividing the convergence, and diversity preservation operations into independent components. Following the proposed update structure, a weighted convergence factor is introduced to serve the convergence strategy, whilst a diversity preservation strategy is built to uniformly distribute the particles in the searched space based on a proposed multidimensional local sparseness degree indicator. By this means, MPSO-EBCD is able to balance convergence and diversity with specific parameters in independent operators. Experimental results on LSMOP benchmarks and a voltage transformer optimization problem demonstrate the competitiveness of the proposed algorithm compared to several state-of-the-art MOEAs.},

keywords = {Complexity theory, Convergence, Cybernetics, diversity, Estimation, large-scale multiobjective optimization, multidimensional local sparseness, Optimization, Particle swarm optimization, particle swarm optimization (PSO), Weight measurement, weighted convergence factor (WCF)},

pubstate = {published},

tppubtype = {article}

}