论文信息 - Optimized Fuzzy Market-Based Solution to the Multiple Traveling Salesmen Problem using Particle Swarm Optimization

Optimized Fuzzy Market-Based Solution to the Multiple Traveling Salesmen Problem using Particle Swarm Optimization

The Multiple Traveling Salesmen Problem (MTSP) is a well-known combinatorial optimization problem, in which a set of locations is to be visited exactly once by a collection of traveling agents. The goal is to either minimize the sum of all tour lengths or the longest tour. This problem has high sensitivity to the perfect knowledge of city locations; however, in many applications, e.g. military or search missions, locations of the tasks is known only up to some level of accuracy. We solve the MTSP using a market-based solution (MBS) in which agents bid for tasks and trade them amongst themselves based on the additional cost of traveling to an additional location. By using a fuzzy cost instead of an crisp cost, taking into account the level of uncertainty in the locations, the sensitivity of the problem to task locations can be reduced. Particle Swarm Optimization (PSO) is used for optimizing the membership functions of the fuzzy cost used in the MBS. We show that the results of the optimized fuzzy market are better than using a crisp cost.

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