本研究利用彈性區帶架構(Flexible Bay Structure, FLB)與改良式蟻群演算法(Hybrid Ant Colony Optimization, HACO)求解不等面積設施佈置問題。改良式蟻群演算法係以蟻群最佳化演算法(Ant Colony Optimization, ACO)為基礎,結合免疫演算法(Clonal Selection Algorithm, CSA)中的四項特性,即複製(clone)、突變(mutate)、記憶細胞與抑制細胞,加強蟻群最佳化演算法的起始解搜尋與蟻群間的差異性。 本研究採用數個國際測試例題來測試改良式蟻群演算法之演算效率,並與過去文獻中的國際已知最佳解結果進行比較。經實驗分析後,在部分國際測試例,改良式蟻群演算法可求得比目前國際最佳解相同甚至是更好的解且改良式蟻群演算法之求解時間較蟻群最佳化演算法的求解時間少。
In this thesis, a flexible bay structure (FBS) and hybrid ant colony algorithm (HACO) are proposed for solving unequal-area facility layout problem. Hybrid ant colony algorithm is based on ant colony optimization (ACO) and ACO are combined with clonal selection algorithm (CSA). Four characteristics of CSA, clone, mutation, memory cells, and suppressor cells, are introduced to improve the solution quality of initial solutions and to increase differences among each ant solution. Several international benchmark problems are used to test the algorithm efficiency of HACO. Compared with other studies of unequal-area facility layout problem, HACO can obtain the same or better solutions to some benchmark problems. In addition, HACO can get the same solution as ACO with less computational time.
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