Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions

2019 ◽  
Vol 23 (4) ◽  
pp. 718-731 ◽  
Author(s):  
Yulian Cao ◽  
Han Zhang ◽  
Wenfeng Li ◽  
Mengchu Zhou ◽  
Yu Zhang ◽  
...  
2014 ◽  
Vol 651-653 ◽  
pp. 2159-2163
Author(s):  
Jia Xing You ◽  
Ji Li Chen ◽  
Ming Gang Dong

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.


2021 ◽  
Vol 25 (10) ◽  
pp. 7143-7154
Author(s):  
Serkan Kaya ◽  
Abdülkadir Gümüşçü ◽  
İbrahim Berkan Aydilek ◽  
İzzettin Hakan Karaçizmeli ◽  
Mehmet Emin Tenekeci

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