Integrated Particle Swarm and Evolutionary Algorithm Approaches to the Quadratic Assignment Problem
This paper introduces three integrated hybrid approaches that apply a combination of Hierarchical Particle Swarm Optimization (HPSO) and Evolutionary Algorithms (EA) to the Quadratic Assignment Problem (QAP). The approaches maintain a single population. In the first approach, Alternating HPSO-EA (AHE), the population alternates between applying HPSO and EA in successive generations. In the second, more integrated approach, Integrated HPSO-EA (IHE), each population element chooses to apply one of the two algorithms in each generation with some probability. An element applying HPSO in a given generation can be influenced by an element applying EA in that generation, and vice versa. Thus, within the same generation, some elements act as HPSO particles and others as EA population members, and yet the entire population still cooperates. In the third approach, we present a Social Evolutionary Algorithm (SEA), in which the population applies EA, and each population element can choose to apply the PSO-style social mutation operator in each generation with some probability. The three approaches are compared to HPSO and EA using 31 instances of varying size from the QAP instance library.
A. M. Helal et al., "Integrated Particle Swarm and Evolutionary Algorithm Approaches to the Quadratic Assignment Problem," Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (2017, Honolulu, HI), Institute of Electrical and Electronics Engineers (IEEE), Nov 2017.
The definitive version is available at https://doi.org/10.1109/SSCI.2017.8280797
2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 (2017: Nov. 27-Dec. 1, Honolulu, HI)
Electrical and Computer Engineering
Intelligent Systems Center
Keywords and Phrases
Artificial intelligence; Combinatorial optimization; Hierarchical particle swarm optimization; Hybrid approach; Integrated approach; Mutation operators; Particle swarm; Quadratic assignment problems; Particle swarm optimization (PSO)
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Nov 2017