Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems

• Published in: Complex & intelligent systems Vol. 11; no. 5; pp. 211 - 21
• Main Authors: Chen, Yanling, Wei, Jingyi, Luo, Tao, Zhou, Jie
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.05.2025; Springer Nature B.V; Springer
• Subjects: Algorithms; Ant colony optimization; Ant colony optimization algorithm; Capacitated vehicle routing planning (CVRP); Capacity adjustment operator; Complexity; Computational Intelligence; Constraints; Crossover operator; Data Structures and Information Theory; Engineering; Genetic algorithms; Logistics; Multi-objective optimization; Multiple objective analysis; Operators; Original Article; Pareto optimization; Particle swarm optimization; Route optimization; Search algorithms; Sorting algorithms; Vehicle routing; Capacitated vehicle routing planning (CVRP); Multi-objective optimization; Crossover operator; Ant colony optimization algorithm; Capacity adjustment operator
• ISSN: 2199-4536; 2198-6053; 2198-6053
• DOI: 10.1007/s40747-025-01826-8

Vehicle routing is a critical issue in the logistics and distribution industry. In practical applications, optimizing vehicle capacity allocation can significantly improve route optimization performance and service coverage. However, solving this problem remains challenging due to the complex constraints involved. Therefore, to address this real-world challenge, a novel intelligent optimization method, multi-objective capacity adjustment ant colony optimization algorithm (MCAACO), is proposed, which integrates advanced multi-objective optimization strategies, including capacity adjustment operators and crossover operators. Combined with pheromone updating and Pareto front-end optimization, the method effectively resolves the conflict between vehicle capacity constraints and multi-objective optimization. To further enhance the algorithm’s performance, dynamic pheromone updating mechanisms and elite individual retention strategies are proposed. Additionally, an adaptive parameter adjustment strategy is designed to balance global search and local exploitation capabilities. Through a series of experiments, it is demonstrated that compared to multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II), and multi-objective sparrow search algorithm (MOSSA), the proposed MCAACO significantly reduces travel paths by an average of 3.05% and increases vehicle service coverage by an average of 3.2%, while satisfying vehicle capacity constraints. Experimental indicators demonstrate that the breakthrough algorithm significantly addresses the issues of high costs and low efficiency prevalent in the practical logistics distribution industry.