Multi-criteria Human Resource Allocation for Optimization Problems Using Multi-objective Particle Swarm Optimization Algorithm

2008 ◽  
Author(s):  
Zhengyuan Jia ◽  
Lihua Gong
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Human Resource ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective

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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