Integrated particle swarm and evolutionary algorithm approaches to the quadratic assignment problem
| Autor: | Ayah Helal, Ashraf M. Abdelbar, Islam Elnabarawy, Enas Jawdat, Donald C. Wunsch |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
education.field_of_study Computer science Quadratic assignment problem Population Probabilistic logic Evolutionary algorithm Particle swarm optimization 020206 networking & telecommunications 02 engineering and technology Integrated approach 0202 electrical engineering electronic engineering information engineering Combinatorial optimization 020201 artificial intelligence & image processing Element (category theory) education |
| Zdroj: | SSCI |
| DOI: | 10.1109/ssci.2017.8280797 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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