TY - JOUR

T1 - An ant colony system-based hybrid algorithm for square root concave cost transhipment problems

AU - Yan, S.

AU - Shih, Y. L.

AU - Wang, C. L.

N1 - Funding Information:
This research was supported by a grant (NSC 94-2211-E-008-024) from the National Science Council of Taiwan.

PY - 2010/11

Y1 - 2010/11

N2 - Concave cost transhipment problems are difficult to optimally solve for large-scale problems within a limited period of time. Recently, some modern meta-heuristics have been employed for the development of advanced local search based or population-based stochastic search algorithms that can improve the conventional heuristics. Besides these meta-heuristics, the ant colony system algorithm is a population-based stochastic search algorithm which has been used to obtain good results in many applications. This study employs the ant colony system algorithm, coupled with some genetic algorithm and threshold accepting algorithm techniques, to develop a population based stochastic search algorithm for efficiently solving square root concave cost transhipment problems. The developed algorithms are evaluated with a number of problem instances. The results indicate that the proposed algorithm is more effective for solving square root concave cost transhipment problems than other recently designed local search based algorithms and genetic algorithm.

AB - Concave cost transhipment problems are difficult to optimally solve for large-scale problems within a limited period of time. Recently, some modern meta-heuristics have been employed for the development of advanced local search based or population-based stochastic search algorithms that can improve the conventional heuristics. Besides these meta-heuristics, the ant colony system algorithm is a population-based stochastic search algorithm which has been used to obtain good results in many applications. This study employs the ant colony system algorithm, coupled with some genetic algorithm and threshold accepting algorithm techniques, to develop a population based stochastic search algorithm for efficiently solving square root concave cost transhipment problems. The developed algorithms are evaluated with a number of problem instances. The results indicate that the proposed algorithm is more effective for solving square root concave cost transhipment problems than other recently designed local search based algorithms and genetic algorithm.

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U2 - 10.1080/03052150903563751

DO - 10.1080/03052150903563751

M3 - Article

AN - SCOPUS:77958170245

VL - 42

SP - 983

EP - 1001

JO - Engineering Optimization

JF - Engineering Optimization

SN - 0305-215X

IS - 11

ER -