Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

2013 ◽  
Vol 20 (6) ◽  
pp. 1572-1581 ◽  
Author(s):  
Yue Tan ◽  
Guan-zheng Tan ◽  
Shu-guang Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Redundancy Allocation ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Allocation Problems

Hybrid Particle Swarm Optimization Algorithm Merging Simulated Annealing and Mountain-Climb Searching

2014 ◽  
Vol 651-653 ◽  
pp. 2159-2163
Author(s):  
Jia Xing You ◽  
Ji Li Chen ◽  
Ming Gang Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Local Search ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

To solve the problem of standard particle swarm optimization (PSO) easy turn to premature convergence and poor ability in local search, this paper present a hybrid particle swarm optimization algorithm merging simulated annealing (SA) and mountain-climb. During the running time, the algorithm use the pso to find the global optimal position quickly, take advantage of the Gaussian mutation and mountain-climb strategy to enhance local search ability, and combine with SA to strengthen the population diversity to enable particles to escape from local minima. Test results on several typical test functions show that this new algorithm has a significant improve in searching ability and effectively overcome the premature convergence problem.

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