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.

scholarly journals Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Weiwei Yu ◽  
Li Zhang ◽  
Chengwang Xie
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mean Value ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cooperative Strategies ◽  
Mutation Strategy

Many-objective optimization problems (MaOPs) refer to those multi-objective problems (MOPs) withmore than three objectives. In order to solve MaOPs, a multi-objective particle swarm optimization algorithm based on new fitness assignment and multi cooperation strategy(FAMSHMPSO) is proposed. Firstly, this paper proposes a new fitness allocation method based on fuzzy information theory to enhance the convergence of the algorithm. Then a new multi criteria mutation strategy is introduced to disturb the population and improve the diversity of the algorithm. Finally, the external files are maintained by the three-point shortest path method, which improves the quality of the solution. The performance of FAMSHMPSO algorithm is evaluated by evaluating the mean value, standard deviation and IGD+ index of the target value on dtlz test function set of different targets of FAMSHMPSO algorithm and other five representative multi-objective evolutionary algorithms. The experimental results show that FAMSHMPSO algorithm has obvious performance advantages in convergence, diversity and robustness.

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