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为了更好地求解非线性多目标联合信号配时优化模型，提出一种多策略的麻雀搜索算法(MS-ISSA)。首先引入Sinusoidal混沌映射，利用其随机性和遍历性的特点有效增加种群的多样性和质量；其次设定动态权重策略，平衡算法的全局探索和局部搜索；最后引入Levy飞行，利用其跳跃性特点探索更多解空间，提高算法的全局寻优能力，并使用9个基准函数对MS-ISSA进行测试，验证了算法的优越性。结合实际案例，应用MS-ISSA求解信号的配时优化模型，对比现状方案、Webster方案、遗传算法(GA)方案和传统的SSA方案，通过MS-ISSA得到的配时方案使延误降低16.8%,汽车尾气排放减少17.9%,交叉口的通行状况得到明显改善，验证了MS-ISSA在交通信号配时优化方面的有效性。
Abstract：In order to better solve the nonlinear multi-objective joint signal timing optimization model, a multi-strategy sparrow search algorithm(MS-ISSA) is proposed. Firstly, sinusoidal chaotic mapping is introduced to utilize its randomness and traversal characteristics, effectively increasing population diversity and quality. Secondly, a dynamic weight strategy is set to balance the global exploration and local search of the algorithm. Finally, Levy Flight was introduced to explore more solution spaces and improve the algorithm's global optimization ability by utilizing its jumping characteristics. Nine benchmark functions were used to test the superiority of MS-ISSA. Based on practical cases, MS-ISSA was applied to solve the signal timing optimization model. Compared with the current scheme, Webster scheme, genetic algorithm(GA) scheme, and traditional SSA scheme, the timing scheme obtained through MS-ISSA reduced delay by 16.8%,reduced automobile exhaust emissions by 17.9%,and significantly improved the traffic conditions at intersections. This verified the effectiveness of MS-ISSA in optimizing traffic signal timing.
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基本信息:
DOI：10.13291/j.cnki.djdxac.2025.03.003
中图分类号:U491.54
引用信息:
[1]裴玉龙,杜小敏.基于多策略改进麻雀算法的交通信号配时优化研究[J].大连交通大学学报,2025,46(03):27-36.DOI:10.13291/j.cnki.djdxac.2025.03.003.
基金信息:
国家重点研发计划(2017YFC0803901)