A directional-biased tabu search algorithm for multi-objective unconstrained binary quadratic programming problem

Unconstrained binary quadratic programming problem (UBQP) consists in maximizing a quadratic 0-1 function. It is a well known NP-hard problem and is considered as a unified model for a variety of combinatorial optimization problems. Recently, a multi-objective UBQP (mUBQP) is defined and a set of mU...

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Vydané v:2013 Sixth International Conference on Advanced Computational Intelligence (ICACI) s. 281 - 286
Hlavní autori: Ying Zhou, Jiahai Wang, Jian Yin
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2013
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Benchmark testing
Optimization
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Shrnutí:Unconstrained binary quadratic programming problem (UBQP) consists in maximizing a quadratic 0-1 function. It is a well known NP-hard problem and is considered as a unified model for a variety of combinatorial optimization problems. Recently, a multi-objective UBQP (mUBQP) is defined and a set of mUBQP instances is proposed. This paper proposes a directional-biased tabu search algorithm (DTS) for mUBQP problem. In the beginning of the search, DTS optimizes the problem for each objective function to obtain extreme solutions. If extreme solution for one objective function cannot be further improved, the search gradually changes the direction and optimizes the problem along the new directions. The proposed algorithm is tested on 50 mUBQP benchmark instances, and experimental results show that DTS can obtain better solutions than the previous state-of-the-art algorithm for the mUBQP cases.
DOI:10.1109/ICACI.2013.6748517