Using Cluster based Global Search Strategy on Multi-Objective Particle Swarm Optimization
碩士 === 國立東華大學 === 電機工程學系 === 94 === Recently, there have been several studies to extend Particle Swarm Optimization (PSO) algorithm to handle multi-objective problems (MOPs). In this thesis, we incorporate cluster based global search into a Multi-Objective Particle Swarm Optimization (MOPSO) algorit...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/61453521648208032181 |
| Summary: | 碩士 === 國立東華大學 === 電機工程學系 === 94 === Recently, there have been several studies to extend Particle Swarm Optimization (PSO) algorithm to handle multi-objective problems (MOPs). In this thesis, we incorporate cluster based global search into a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. We call it CGS-MOPSO. By using the clustering mechanism, we obtain better spread between the non-dominated solutions we found. Then we use the center of a circle method for selecting the appropriate global best solution for each particle of the population. This approach is validated by using five benchmarks and compare with the Sigma method. Experiments adopted three performance measurement methods including the general distance, diversity metric, and maximum spread for comparing CGS-MOPSO with Sigma method. CGS-MOPSO shows the smaller difference with real Pareto front and better spread than Sigma method.
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