Global Optimum Distance Evaluated Particle Swarm Optimization for Combinatorial Optimization Problem
Based on the mechanism of Particle Swarm Optimization (PSO) measurement process, every particle estimates the global minimum/maximum. Particles communicate among them to update and improve the solution during the search process. However, the PSO is only capable to solve continuous numerical optimiza...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Malaysia Pahang
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/15530/1/P126%20pg943-950.pdf http://umpir.ump.edu.my/id/eprint/15530/ http://ee.ump.edu.my/ncon/wp-content/uploads/2016/10/Proceeding-NCON-PGR-2016.zip |
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| Summary: | Based on the mechanism of Particle Swarm Optimization (PSO) measurement process, every particle estimates the global minimum/maximum. Particles communicate among them to update and improve the solution during the search process. However, the PSO is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, a new global optimum distance evaluated approach is proposed and combined with PSO. A set of traveling salesman problems (TSP) are used to evaluate the performance of the proposed global optimum distance evaluated PSO (GO-DEPSO). Based on the analysis of experimental results, we found that the proposed DEPSO is capable to solve discrete optimization problems using TSP. |
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