面向电商物流配送与营销策略优化的DDMOPSO算法设计及性能验证
Design and Performance Verification of DDMOPSO Algorithm for Optimization of E-Commerce Logistics Distribution and Marketing Strategies
DOI: 10.12677/ecl.2026.1591053, PDF,    科研立项经费支持
作者: 徐江燕, 陈四万：贵州大学数学与统计学院，贵州 贵阳；刘衍民*：遵义师范学院数学学院，贵州 遵义
关键词: 电子商务；多目标优化；DDMOPSO粒子群算法；物流配送优化；营销策略优化；E-Commerce； Multi-Objective Optimization； DDMOPSO Particle Swarm Algorithm； Logistics Distribution Optimization； Marketing Strategy Optimization
摘要: 针对电子商务领域物流配送路径优化、营销策略精准化等核心场景的多目标优化需求，传统多目标粒子群算法(MOPSO)在收敛性与多样性的平衡上存在明显不足，难以高效满足电商平台精细化运营、提质增效的实际需求。为此，本文设计并验证了一种面向电商场景的DDMOPSO算法。采用ZDT与UF系列基准测试函数模拟电商实际多目标冲突场景，将该算法与八种主流多目标优化算法开展对比实验，验证其综合性能。实验结果表明，DDMOPSO算法具备更优的收敛速度和全局搜索能力，能够有效平衡算法的收敛性与多样性，可高效解决电商物流配送中成本与时效、营销策略中推荐精准度与覆盖度的多目标平衡问题。该算法为电商领域复杂多目标优化问题提供了高效可靠的技术支撑，有助于推动电商平台精细化运营、降低运营成本、提升用户体验，进而助力电商经济提质增效。
Abstract: In response to the multi-objective optimization demands in core scenarios such as logistics distribution path optimization and marketing strategy precision in the e-commerce field, the traditional multi-objective particle swarm optimization (MOPSO) algorithm shows significant deficiencies in balancing convergence and diversity, making it difficult to efficiently meet the actual needs of e-commerce platforms for refined operation, quality improvement, and efficiency enhancement. Therefore, this paper designs and validates a DDMOPSO algorithm tailored for e-commerce scenarios. By using the ZDT and UF series benchmark test functions to simulate the actual multi-objective conflict scenarios in e-commerce, the algorithm is compared with eight mainstream multi-objective optimization algorithms to verify its comprehensive performance. The experimental results show that the DDMOPSO algorithm has a superior convergence speed and global search capability, effectively balancing the convergence and diversity of the algorithm. It can efficiently solve the multi-objective balance problems in e-commerce logistics distribution, such as cost and timeliness, and in marketing strategies, such as recommendation accuracy and coverage. This algorithm provides efficient and reliable technical support for complex multi-objective optimization problems in the e-commerce field, which is conducive to promoting refined operation of e-commerce platforms, reducing operating costs, and enhancing user experience, thereby contributing to the quality improvement and efficiency enhancement of the e-commerce economy.
文章引用：徐江燕, 刘衍民, 陈四万. 面向电商物流配送与营销策略优化的DDMOPSO算法设计及性能验证[J]. 电子商务评论, 2026, 15(9): 742-749. https://doi.org/10.12677/ecl.2026.1591053

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