Meta-heuristic algorithms proved to find optimal solutions for combinatorial problems in many\ndomains. Nevertheless, the efficiency of these algorithms highly depends on their parameter settings.\nIn fact, finding appropriate settings of the algorithm�s parameters is considered to be a nontrivial\ntask and is usually set manually to values that are known to give reasonable performance.\nIn this paper, Ant Colony Optimization with Parametric Analysis (ACO-PA) is developed to overcome\nthis drawback. The main feature of the ACO-PA is the ability of deciding the appropriate parameter\nvalues within the predefined parameter variations. Besides, a new approach which enables\nthe pheromone information value to be proportional to the heuristic information value is\nintroduced. The effectiveness of the proposed algorithm is investigated through the application of\nthe algorithm to the construction site layout problems taken from the state-of-art. Results show\nthat the ACO-PA can reduce transportation cost up to 16.8% compared to the site layouts generated\nby Genetic Algorithms and basic ACO. Moreover, the effects of parameter settings on the generated\nsolutions are investigated.
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