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An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP)
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AI
DOI：10.1016/j.amc.2007.09.070.png)
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Abstract
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The multi-objective resource allocation problem (MORAP) addresses the important issue which seeks to find the expected objectives by allocating the limited amount of resource to various activates. Resources may be manpower, assets, raw material or anything else in limited supply which can be used to accomplish the goals. The goals may be objectives (i.e., minimizing costs, or maximizing efficiency) usually driven by specific future needs. In this paper, in order to obtain a set of Pareto solution efficiently, we proposed a modified version of ant colony optimization (ACO), in this algorithm we try to increase the efficiency of algorithm by increasing the learning of ants. Effectiveness and efficiency of proposed algorithm was validated by comparing the result of ACO with hybrid genetic algorithm (hGA) which was applied to MORAP later. (C) 2007 Elsevier Inc. All rights reserved.
Keywords:
ant colony optimization
multi-objective optimization model
multi-objective resources allocation problem (MORAP)
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3.4
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2.3W
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3.3W
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