Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing

Abstract

In this article, an improved slime mould algorithm (SMA-CSA) is proposed for solving global optimization and the capacitated vehicle routing problem (CVRP). This improvement is based on the mixed-strategy optimization of Cauchy mutation and simulated annealing to alleviate the lack of global optimization capability of the SMA. By introducing the Cauchy mutation strategy, the optimal solution is perturbed to increase the probability of escaping from the local extreme value; in addition, the annealing strategy is introduced, and the Metropolis sampling criterion is used as the acceptance criterion to expand the global search space to enhance the exploration phase to achieve optimal solutions. The performance of the proposed SMA-CSA algorithm is evaluated using the CEC 2013 benchmark functions and the capacitated vehicle routing problem. In all experiments, SMA-CSA is compared with ten other state-of-the-art metaheuristics. The results are also analyzed by Friedman and the Wilcoxon rank-sum test. The experimental results and statistical tests demonstrate that the SMA-CSA algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the capacitated vehicle routing problem demonstrate its efficiency and discrete solving ability.

Optimizing Heterogeneous Vehicle Routes for Urban Distribution Considering the Three-Dimensional Bin Packing Problem of Electric Meters

“…Within the realm of existing research, urban distribution has consistently been characterized by stringent delivery time constraints. In one notable instance, X. Zhang [23] introduced an improved slime mould algorithm (SMA-CSA) is proposed for solving global optimization and the CVRP. Recognize the imbalance of vehicle loads, C. Wang [24] not only strives to minimize the total length of all routes, but also strives to reduce the difference in load allocated to each vehicle, achieving a more efficient and balanced allocation of resources.…”

Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm

“…Additionally, in comparison to the Gaussian and t-distributions, the Cauchy distribution's origin peak is smaller. This smaller peak guides individuals to use a lesser time trying to find the optimal position [33]. Therefore, the Cauchy mutation exhibits a stronger perturbation and is more conducive to helping the slime mold population escape local optima.…”