改进正余弦优化算法及在公交排班模型中应用

Application of Improved Sines and Cosines Optimization Algorithm in Bus Scheduling Model

针对正余弦算法在搜索过程中存在收敛精度低、易陷入局部最优等缺点,提出一种基于正态变异自适应的改进正余弦算法(MSCA)。首先,将变异操作引入SCA算法,进行种群初始化;其次,引入惯性权重来修正位置更新方程,保留更多的信息和产生有前途的候选解;提出一种平衡勘探开发的非线性转换参数递减策略,在搜索过程中跳出局部最优解状态;最后,改进基于正态变异算子的位置更新策略,增加局部搜索空间。再选取26个国际标准测试函数对改进的算法进行测试,结果表明,MSCA算法在收敛精度、收敛速度和收敛稳定性上,更优于其他改进算法。除此之外,还将改进后的MSCA应用在城市公交排班中,通过实验仿真得到改进的MSCA的最优目标函数值优于原有算法SCA,以及公交排班符合客流的实际分布。

Aiming at the shortcomings of the sine-cosine algorithm in the search process,such as low convergence accuracy and easy to fall into local optimum,this paper proposes an improved sine-cosine algorithm(MSCA)based on normal mutation adaptation.First of all,the mutation operation is introduced into the SCA algorithm to initialize the population;Secondly,the inertial weights are introduced to modify the position update equation to retain more information and generate promising candidate solutions.A non-linear conversion parameter decline strategy for balanced exploration and development is proposed,which jumps out of the local optimal solution state during the search process.Finally,the location update strategy based on the normal mutation operator is improved,and the local search space is increased.Then 26 international standard test functions are seleted to test the improved algorithm.The results show that the MSCA algorithm is better than other improved algorithms in terms of convergence acuracy,convergence speed and convergence stability.In addition,the improved MSCA is also used in the practical application of urban bus scheduling.The optimal objective function value of the improved MSCA obtained through experimental simulation is better than the original algorithm SCA,and the bus scheduling is in line with the actual distribution of passenger flow.It fully reflects the common interests of bus operators and passengers.