Particle Swarm Algorithm variants for the Quadratic Assignment Problems - A probabilistic learning approach

作者：

Highlights：

• A new probability-based approach is proposed for the learning in discrete PSO.

• A generic framework is proposed to discretize PSO and its variants.

• Five well-known PSO variants are discretized based on the proposed framework.

• Comparative evaluation and search landscapes analysis is presented.

摘要

•A new probability-based approach is proposed for the learning in discrete PSO.•A generic framework is proposed to discretize PSO and its variants.•Five well-known PSO variants are discretized based on the proposed framework.•Comparative evaluation and search landscapes analysis is presented.

论文关键词：Combinatorial optimization,Fitness landscapes,Meta-heuristics,Particle Swarm Optimization (PSO),Quadratic Assignment Problem (QAP),Search space analysis,Swarm intelligence

论文评审过程：Received 24 June 2015, Revised 18 September 2015, Accepted 20 September 2015, Available online 30 September 2015, Version of Record 10 November 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.09.032