Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems
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| Publicado en: | Complex & Intelligent Systems vol. 11, no. 5 (May 2025), p. 211 |
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| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2199-4536 | ||
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| 024 | 7 | |a 10.1007/s40747-025-01826-8 |2 doi | |
| 035 | |a 3178013175 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 245 | 1 | |a Mcaaco: a multi-objective strategy heuristic search algorithm for solving capacitated vehicle routing problems | |
| 260 | |b Springer Nature B.V. |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Pareto optimization | ||
| 653 | |a Search algorithms | ||
| 653 | |a Particle swarm optimization | ||
| 653 | |a Operators | ||
| 653 | |a Algorithms | ||
| 653 | |a Logistics | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Vehicle routing | ||
| 653 | |a Route optimization | ||
| 653 | |a Ant colony optimization | ||
| 653 | |a Constraints | ||
| 653 | |a Sorting algorithms | ||
| 773 | 0 | |t Complex & Intelligent Systems |g vol. 11, no. 5 (May 2025), p. 211 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3178013175/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3178013175/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |