A Hybrid Evolutionary Algorithm to Quadratic Three-Dimensional Assignment Problem with Local Search for Many-Core Graphics Processors

Abstract

This paper concerns the quadratic three-dimensional assignment problem (Q3AP), an extension of the quadratic assignment problem (QAP), and proposes an efficient hybrid evolutionary algorithm combining stochastic optimization and local search with a number of crossover operators, a number of mutation operators and an auto-adaptation mechanism. Auto-adaptation manages the pool of evolutionary operators applying different operators in different computation phases to better explore the search space and to avoid premature convergence. Local search additionally optimizes populations of candidate solutions and accelerates evolutionary search. It uses a many-core graphics processor to optimize a number of solutions in parallel, which enables its incorporation into the evolutionary algorithm without excessive increases in the computation time. Experiments performed on benchmark Q3AP instances derived from the classic QAP instances proposed by Nugent et al. confirmed that the proposed algorithm is able to find optimal solutions to Q3AP in a reasonable time and outperforms best known results found in the literature.