Extension of PSO and ACO-PSO algorithms for solving Quadratic Assignment Problems

International Conference on Mechanical, Materials and Renewable Energy

机械制造;材料科学;能源学

Rameshkumar, K.^1

Department of Mechanical Engineering, Amrita University, Bangalore, India^1

关键词: Ant colony algorithms;    Bench-mark problems;    Computational results;    Global bests;    Hybrid approach;    Local search;    PSO algorithms;    Quadratic assignment problems;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/377/1/012192/pdf
DOI  :  10.1088/1757-899X/377/1/012192

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

In this paper, PSO and Ant Colony Optimization inspired PSO (ACO-PSO) algorithms were adopted to solve the Quadratic Assignment Problems. A hybrid approach is adopted in this paper by combining assignment construction with local-search. In the PSO algorithm, solution construction has been carried out by assigning weights to current, particle's best and global best solutions associated with assignment of resources. Velocities which are used to construct the assignments in this approach are similar to the trail intensities considered in the ant colony algorithms. The proposed algorithms have been applied to a set of benchmark problems and the performance of the algorithm is evaluated by testing the obtained results with the results published in the literature. The computational results show that good quality solutions are obtained using the PSO and ACO inspired PSO algorithm.