Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.

Ant Colony Optimization

Routing and charging of electric vehicles: Literature review

Multi Objective Evaluation Between Learning Behavior and Learning Achievement

Adjustable driving force based particle swarm optimization algorithm

AbstractAutomated Guided Vehicle (AGV) path planning is the core technology of warehouse AGV. Reasonable path planning is helpful to maximize the benefits of warehouse space and time. Scholars at home and abroad have already made extensive and in-depth research on warehouse AGV path planning, and have achieved fruitful research results. In this paper, the models and environmental modeling methods of warehouse AGV path planning are summarized. It turned out that the cell method is intuitive and easy to model, the geometric method is safe, but difficult to update, and the artificial potential field method is easy to solve, but easy to fall into local optimum. The optimization methods of genetic algorithm, ant colony algorithm and particle swarm optimization algorithm in AGV path planning are emphatically summarized. It is found that genetic algorithm is suitable for complex and highly nonlinear path planning problems, ant colony algorithm is suitable for discrete path planning problems, and particle swarm algorithm is suitable for real number path planning problems. The research summary of this paper provides reference value for the research of intelligent optimization algorithm of AGV path planning and new ideas for broadening the application field of AGV path planning.KeywordsWarehouseAGVPath planningIntelligent optimization algorithm 

Research Summary of Intelligent Optimization Algorithm for Warehouse AGV Path Planning

Aiming at vehicle routing problem and combining the advantages of ant colony and particle swarm optimization, an intelligent optimization algorithm of adaptive ant colony and particle swarm optimization is proposed. Through the simulation of ant colony and bird swarm intelligence mechanism, the particle swarm algorithm and the ant colony algorithm heuristic strategy are combined, and different search strategies are used in different stages of the algorithm. The adaptive adjustment is adopted, and the feedback information is obtained by dynamic interaction with the environment, thus speeding up the convergence speed, improving the learning ability, avoiding the local optimum, getting the best solution and improving the efficiency. The simulation experiment shows that the algorithm has fast convergence speed, strong optimization ability, and can obtain better optimization results. It has some advantages in solving vehicle routing problem.

Chunyan Jiang

Papers

Research on Vehicle Routing Planning Based on Adaptive Ant Colony and Particle Swarm Optimization Algorithm