A New Mutation Mechanism in the Ant Colony Algorithm
Publish place: The first national conference on meta-heuristic algorithms and their applications in science and engineering
Publish Year: 1393
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
MHAA01_044
تاریخ نمایه سازی: 17 اسفند 1393
Abstract:
Ant Colony Optimization (ACO) is a type of metaheuristic algorithms for optimization problems. The main problem of all metaheuristic algorithms are local optima. In this paper some simple mutation methods are proposed in ACO. The main parameters in using mutation methods are improving answer accuracy and the effect of mutation on runtime. The presented mutation methods can expand searching range and avoid local minima by randomly changing one or more elements of the local best solution, which is similar to the mutation operation in genetic algorithm. As the mutation operation is simple to implement, the performance of MACO is superior with almost the same computational complexity. The proposed methods are applied to TSP in a large dataset and simulation results confirm that the ACO with these methods has much better performance than conventional ACO algorithms
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Authors
keivan borna
Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran,
vahid hajihashemi
Faculty of Engineering, Kharazmi University, Tehran, Iran
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