A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

2018 ◽  
Vol 58 (4) ◽  
pp. 1505-1522 ◽  
Author(s):  
Zhao Liu ◽  
Zeyang Li ◽  
Ping Zhu ◽  
Wei Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Constrained Optimization Problems ◽  
Swarm Optimization

A Constrained Particle Swarm Optimization Algorithm with Oracle Penalty Method

2013 ◽  
Vol 303-306 ◽  
pp. 1519-1523 ◽  
Author(s):  
Ming Gang Dong ◽  
Xiao Hui Cheng ◽  
Qin Zhou Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Penalty Method ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Swarm Optimization

To solve constrained optimization problems, an Oracle penalty method-based comprehensive learning particle swarm optimization (OBCLPSO) algorithm was proposed. First, original Oracle penalty was modified. Secondly, the modified Oracle penalty method was combine with comprehensive learning particle swarm optimization algorithm. Finally, experimental results and comparisons were given to demonstrate the optimization performances of OBCLPSO. The results show that the proposed algorithm is a very competitive approach for constrained optimization problems.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

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