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

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yanling Chen, Jingyi Wei, Tao Luo, Jie Zhou
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引用次数: 0

Abstract

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.

基于多目标策略启发式搜索算法的车辆路径问题求解
车辆路线是物流配送行业的一个关键问题。在实际应用中,优化车辆容量分配可以显著提高路线优化性能和服务覆盖率。然而,由于涉及到复杂的约束,解决这个问题仍然具有挑战性。因此,为了解决这一现实挑战,提出了一种新的智能优化方法——多目标容量调整蚁群优化算法(MCAACO),该算法集成了先进的多目标优化策略,包括容量调整算子和交叉算子。该方法将信息素更新与Pareto前端优化相结合,有效地解决了车辆容量约束与多目标优化之间的冲突。为了进一步提高算法的性能,提出了动态信息素更新机制和精英个体保留策略。此外,设计了一种自适应参数调整策略,以平衡全局搜索和局部开发能力。通过一系列实验表明,与多目标粒子群算法(MOPSO)、非支配排序遗传算法II (NSGA-II)和多目标麻雀搜索算法(MOSSA)相比,所提出的MCAACO在满足车辆容量约束的情况下,平均减少了3.05%的出行路径,平均增加了3.2%的车辆服务覆盖率。实验指标表明,该突破性算法显著解决了实际物流配送行业普遍存在的成本高、效率低的问题。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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