An adaptive simulated annealing and artificial fish swarm algorithm for the optimization of multi-depot express delivery vehicle routing

Abstract

In this paper, the Capacitated Vehicle Routing Problem (CVRP) of multi-depot express delivery is investigated based on the actual express delivery business in Beijing and driving intention-based road network. An Adaptive Simulated Annealing and Artificial Fish Swarm Algorithm (A-SAAFSA) is proposed to solve the CVRP. The basic ideas are use a “certainty” probability to accept the worst solution through the Metropolis criterion in the search process, and a strategy of adjusting the swimming direction to avoid falling into the local optimal solution. Moreover, an adaptive visual strategy, which adjusts the visual range adaptively in real time according to the current solution quality, is used to ensure the efficient searching and accuracy of the algorithm. Experimental results show that the A-SAAFSA algorithm outperforms four well-known algorithms, namely simulated annealing and artificial fish swarm algorithm, artificial fish swarm algorithm, simulated annealing algorithm, and genetic algorithm.

Review of research on vehicle routing problems

“…Xia et al [5] apply an artificial bee colony algorithm based on adaptive large neighborhood search, which can effectively solve the problems of poor convergence, insufficient exploration ability and long solution time when solving CVRP. Yuan et al [6] put forward an adaptive simulated annealing and artificial fish swarming algorithm (A-SAAFSA) for the optimization of vehicle routing from multiple warehouses to multiple costumers.…”

Product Quality Prediction Based on RBF Optimized by Firefly Algorithm

“…Nature-derived algorithms are the most powerful algorithms for optimization. Recently, swarm intelligence [26], the biologically inspired algorithms, have been studied dramatically, such as firefly swarm optimization algorithm [14], ant colony optimization technique [27], particle swarm optimization (PSO) algorithm [28], artificial fish swarm optimization algorithm [29], and swallow swarm optimization (SSO) [30]. They have been applied to solve various problems, such as healthcare, finance, energy, image thresholding, and others.…”

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

“…The artificial fish swarm algorithm is a population intelligent optimization algorithm. The basic idea is to simulate the foraging behavior, tail chasing behavior, and herd behavior of fish [ [29] , [30] , [31] ]. This algorithm can be used to solve various continuous optimization problems and Combinatorial optimization problems.…”