Multi-Objective Particle Swarm Optimization with Comparison Scheme and New Pareto-Optimal Search Strategy
2014 ◽
Vol 496-500
◽
pp. 1895-1900
Keyword(s):
In the presented article, a novel multi-objective PSO algorithm, RP-MOPSO has been proposed. The algorithm adopts a new comparison scheme for position upgrading. The scheme is simple but effective in improve algorithms convergence speed. A sigma-density strategy of selecting the global best particle for each particle in swarm based on a new solutions density definition is designed. Experimental results on seven functions show that proposed algorithm show better convergence performance than other classical MOP algorithms. Meanwhile the proposed algorithm is more effective in maintaining the diversity of the solutions.
2009 ◽
Vol 57
(11-12)
◽
pp. 1995-2000
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2020 ◽
Vol 13
(6)
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pp. 76-84
2020 ◽
2019 ◽
Vol 11
◽
pp. 184797901986783
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2013 ◽
Vol 12
(01)
◽
pp. 15-41
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2013 ◽
Vol 333-335
◽
pp. 1361-1365
2011 ◽
Vol 474-476
◽
pp. 2229-2233