Modified Local Search Particle Swarm Optimization Algorithm Based on Channel Estimation with VHDL

2018 ◽  
Author(s):  
Ansam Subhi Jabbar ◽  
Ali Kareem Nahar ◽  
Hussain Kareem Khleaf ◽  
Mohammed Jawed Mortada
Keyword(s):  
Particle Swarm Optimization ◽  
Channel Estimation ◽  
Local Search ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization

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.

Solution for flow shop scheduling problems using chaotic hybrid firefly and particle swarm optimization algorithm with improved local search

2021 ◽  
Vol 25 (10) ◽  
pp. 7143-7154
Author(s):  
Serkan Kaya ◽  
Abdülkadir Gümüşçü ◽  
İbrahim Berkan Aydilek ◽  
İzzettin Hakan Karaçizmeli ◽  
Mehmet Emin Tenekeci
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Flow Shop ◽  
Particle Swarm ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Swarm Optimization ◽  
Shop Scheduling

scholarly journals A Quantum Particle Swarm Optimization Algorithm with Teamwork Evolutionary Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guoqiang Liu ◽  
Weiyi Chen ◽  
Huadong Chen ◽  
Jiahui Xie
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Evolutionary Strategy ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Mutation Operators

The quantum particle swarm optimization algorithm is a global convergence guarantee algorithm. Its searching performance is better than the original particle swarm optimization algorithm (PSO), but the control parameters are less and easy to fall into local optimum. The paper proposed teamwork evolutionary strategy for balance global search and local search. This algorithm is based on a novel learning strategy consisting of cross-sequential quadratic programming and Gaussian chaotic mutation operators. The former performs the local search on the sample and the interlaced operation on the parent individual while the descendants of the latter generated by Gaussian chaotic mutation may produce new regions in the search space. Experiments performed on multimodal test and composite functions with or without coordinate rotation demonstrated that the population information could be utilized by the TEQPSO algorithm more effectively compared with the eight QSOs and PSOs variants. This improves the algorithm performance, significantly.

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