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The application of binary accelerated particle swarm optimization method to unconstrained binary quadratic problems

Geng Lin, Jian Guan

Abstract


The unconstrained binary quadratic problem is a classical NP-hard problem in combinatorial optimization having a number of engineering applications. In this paper, we investigate a binary accelerated particle swarm optimization implementation for the unconstrained binary quadratic problem. It hybrids binary accelerated particle swarm optimization and tabu search. The proposed algorithm is tested on 10 benchmark instances from the literature.
The experimental results showed that the proposed algorithm is efficient.

Keywords


accelerated particle swarm optimization, unconstrained binary quadratic problem, tabu search, local search.

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