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A Particle Swarm Optimization Based on Evolutionary Game Theory for Discrete Combinatorial Optimization
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fu, Peihua Wang, Yijie Peng, Yang |
| Copyright Year | 2012 |
| Abstract | This paper presented a new particle swarm optimization based on evolutionary game theory (EPSO) for the traveling salesman problem (TSP) to overcome the disadvantages of premature convergence and stagnation phenomenon of traditional particle swarm optimization algorithm (PSO). In addition ,we make a mapping among the three parts discrete particle swarm optimization (DPSO)、 evolutionary game theory and traveling salesman problem by using replicator dynamics to restrict the behavior of particles. Finally, we give experimental examples in the standard library LIBTSP and the performance analyses of the algorithm. Comparing with the genetic algorithm and basic particle swarm optimization algorithm we show that the EPSO algorithm is effective. |
| Starting Page | 369 |
| Ending Page | 376 |
| Page Count | 8 |
| File Format | PDF HTM / HTML |
| DOI | 10.4156/jcit.vol7.issue21.45 |
| Volume Number | 7 |
| Alternate Webpage(s) | http://www.aicit.org/JCIT/ppl/JCIT2088PPL.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
