School of Mathematics, Statistics and Computer Science, University of Kwazulu-Natal, Durban, South Africa.
Department of Computer Science, Federal University Lafia, Lafia, Nasarawa State, Nigeria.
PLoS One. 2018 Mar 19;13(3):e0193751. doi: 10.1371/journal.pone.0193751. eCollection 2018.
The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.
智能水滴算法是一种基于群体的元启发式算法，灵感来自河流中水滴的特性以及河流流动所导致的环境变化。自作为替代随机优化方法出现以来，该算法已在解决各种组合和功能优化问题中得到应用。本文提出了一种改进的智能水滴算法，用于解决多仓库车辆路径问题。将模拟退火算法引入到所提出的算法中作为局部搜索元启发式算法，以防止智能水滴算法陷入局部最小值，并提高其求解质量。此外，还研究了与使用模拟退火相关的一些潜在问题，包括高计算运行时间和接受标准概率的指数计算。模拟退火技术的接受标准概率的指数计算在计算上是昂贵的。因此，为了最大限度地提高使用模拟退火的智能水滴算法的性能，考虑了一种更好的计算接受标准概率的方法。通过使用 33 个标准测试问题来评估所提出的混合算法的性能，将得到的结果与来自主题文献的四种知名技术提供的解决方案进行比较。实验结果和统计检验表明，新方法在求解质量和消耗的运行时间方面具有出色的性能。此外，该算法适用于解决大规模问题。
