NDLI: PMACO: A pheromone-mutation based ant colony optimization for traveling salesman problem

Content Provider	IEEE Xplore Digital Library
Author	Shokouhifar, M. Sabet, S.
Copyright Year	2012
Description	Author affiliation: Scientific Association of Electrical Engineering, Islamic Azad University of Central Tehran Branch, Tehran, Iran (Sabet, S.) \|\| Electrical and Computer Engineering Department, Shahid Beheshti University, Tehran, Iran (Shokouhifar, M.)
Abstract	Traveling salesman problem (TSP) is a popular routing problem, which is a sub-problem of many application domains such as transportation, network communication, vehicle routing and integrated circuits designs. Among many approaches which have been proposed for TSP so far, evolutionary algorithms effectively applied to solve it, and attempt to avoid trapping in local minima. In this paper, a hybrid approach (named PMACO) is proposed for TSP, which is based on ant colony optimization (ACO) and the mutation operators in genetic algorithm (GA). In this manner, in the each iteration a hybrid mutation scheme is applied to search the neighborhood area of the solution corresponding with the two best ants (the best current ant, and the total best ant found so far). The population is divided to two parts: the first is regenerated according to the pheromone rule in ACO, and the last is generated by applying the proposed hybrid mutation scheme. Also in this paper, the initial population was not considered randomly; it was generated according to nearest neighborhood rule, which force the ants toward better solutions, and leads to reduce the running time. Finally, we compared the performance of PMACO, with ACO and GA separately. For each algorithm, the various parameters and operators were tested, and the bests were chosen to tune that. Experimental results were carried out on benchmarks from the TSPLIB for validation.
Starting Page	1
Ending Page	5
File Size	981185
Page Count	5
File Format	PDF
ISBN	9781467314466
e-ISBN	9781467314480
DOI	10.1109/INISTA.2012.6247040
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Genetic algorithm (GA) Traveling salesman problem (TSP) Cities and towns Traveling salesman problems Routing Hybrid power systems Mutation Ant colony optimization (ACO) Optimization Genetic algorithms
Content Type	Text
Resource Type	Article

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