Particle swarm optimization methods and local heuristics for solving the multiple traveling salesman problem

This paper presents the development and evaluation of a method for solving the Multiple Traveling Salesman Problem (mTSP), with the objective of minimizing the maximum route length (“minimax” optimization). The study addresses the combinatorial route-space arising from distributing cities among multiple agents, requiring balanced workload distribution to avoid overloading individual routes. The novelty of the proposed approach lies in creating a discrete analogue of the classical Particle Swarm Optimization (PSO) algorithm adapted specifically for permutation-based route representations, and integrating it with local heuristic procedures and the Ant Colony Optimization (ACO) algorithm. The proposed method transforms the original mTSP into a classical single-agent Traveling Salesman Problem (TSP) by introducing artificial (dummy) depots, thus allowing an unambiguous separation of the overall route into individual segments for each agent. A key element of the solution involves adapting the PSO algorithm through novel discrete operations, such as computing the minimal sequence of exchanges (transpositions) between permutations, scaling velocity, and applying this velocity to routes. This approach ensures efficient exploration of the combinatorial solution space and prevents premature convergence of the algorithm. The experimental study was conducted on benchmark instances from the TSPLIB library (eil51.tsp, berlin52.tsp, eil76.tsp, rat99.tsp) for the TSP, comparing two scenarios: a classical PSO with random initialization and a hybrid PSO_ACO method where the ACO algorithm is used to generate the initial population. The results demonstrate a significant improvement in the minimax criterion compared to CPLEX, LKH3, OR-Tools as well as state-of-the-art approaches DAN, NCE, and EA, confirming the effectiveness of the proposed solution. The practical importance of this research lies in potential applications of the developed algorithm in logistics, transport planning, network traffic management, and other domains where optimal resource allocation is crucial. The proposed method is valuable for specialists in optimization, algorithmic modeling, and practitioners developing planning and management systems.

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