A Novel Multistrategy-Based Differential Evolution Algorithm and Its Application

Abstract

To address the poor searchability, population diversity, and slow convergence speed of the differential evolution (DE) algorithm in solving capacitated vehicle routing problems (CVRP), a new multistrategy-based differential evolution algorithm with the saving mileage algorithm, sequential encoding, and gravitational search algorithm, namely SEGDE, is proposed to solve CVRP in this paper. Firstly, an optimization model of CVRP with the shortest total vehicle routing is established. Then, the saving mileage algorithm is employed to initialize the population of the DE to improve the initial solution quality and the search efficiency. The sequential encoding approach is used to adjust the differential mutation strategy to legalize the current solution and ensure its effectiveness. Finally, the gravitational search algorithm is applied to calculate the gravitational relationship between points to effectively adjust the evolutionary search direction and further improve the search efficiency. Four CVRPs are selected to verify the effectiveness of the proposed SEGDE algorithm. The experimental results show that the proposed SEGDE algorithm can effectively solve the CVRPs and obtain the ideal vehicle routing. It adopts better search speed, global optimization ability, routing length, and stability.

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

“…In order to verify the efficiency of the IWOA-HMOHA algorithm in dealing with high-dimensional problems, we set up 30 dimensions and 300 dimensions and selected the functions in Table 5 for simulation experiments. The best (Best), mean value (Avg) and standard deviation (Std) of the benchmark function values obtained by performing 30 iterations of the algorithm are compared with particle swarm optimization (PSO) [41], differential evolution algorithm (DE) [16], whale optimization algorithm (WOA), multi-verse optimization (MVO) [42], pelican optimization algorithm (POA) [43]. ( ) As seen from table 6 and table 7, the IWOA-HMOHA algorithm shows strong competitiveness in global convergence, search accuracy and convergence speed compared with similar benchmark algorithms in evaluating six typical benchmark functions in low and high dimensions.…”

“…Secondly, aiming at the characteristics of solving such complex NPhard problems [14], the linear inertia weight in the Whale Optimization Algorithm (WOA) is improved to the adaptive inertia weight, which is helpful to balance the search performance of the algorithm in the early and late stages [15]. Furthermore, inspired by the differential evolution algorithm (DE) , a difference strategy is introduced in the process of population renewal [16], which is different from the inertial spiral search strategy of the whale algorithm, and reconstructs the feasible solution through mutation and crossover, so as to avoid the problem that the algorithm is easy to fall into local optimum. Finally, through the population classification mechanism, the local development ability of the algorithm in the later stage is enhanced, and the problem of slow algorithm convergence speed is solved.…”

Advanced Machine Learning Applications in Big Data Analytics

“…Wang et al [11] proposed a new algorithm called SEGDE to solve the capacitated vehicle routing problem (CVRP). It combined the saving mileage algorithm (SMA), sequential encoding (SE), and gravitational search algorithm (GSA) to address the problems of the differential evolution (DE) algorithm.…”