Future Generation Computer Systems
Ant Colony Optimization
A new hybrid heuristic approach for solving large traveling salesman problem
Information Sciences—Informatics and Computer Science: An International Journal
Knowledge-based genetic algorithm for university course timetabling problems
International Journal of Knowledge-based and Intelligent Engineering Systems
Review: A review of ant algorithms
Expert Systems with Applications: An International Journal
IEEE Computational Intelligence Magazine
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
IEEE Transactions on Evolutionary Computation
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This paper presents a method to improve the search rate of Max-Min Ant System for the traveling salesman problem. The proposed method gives deviations from the initial pheromone trails by using a set of local optimal solutions calculated in advance. This method aims to build a near optimal solution at high speed by combining the candidate partial solutions contained in the set. Max-Min Ant System has demonstrated impressive performance, but the search rate is relatively low. Considering the generic purpose of stochastic search algorithms, which is to find near optimal solutions subject to time constraints, the search rate is important as well as the solution quality. The experimental results using benchmark problems with 51 to 1002 cities suggested that the proposed method has a faster search rate than Max-Min Ant System; the additional computation cost for calculating local optimal solutions is negligibly small.