An Improved Grey Wolf Optimization Algorithm and its Application in Path Planning

Abstract

Grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems. Therefore, an improved grey wolf optimization algorithm(IGWO) is proposed. The lion optimizer algorithm and dynamic weights are integrated into the original grey wolf optimization algorithm. When the positions of $\alpha $ wolf, $\beta $ wolf, and $\delta $ wolf are updated, the lion optimizer algorithm is used to add disturbance factors to the wolves to give $\alpha $ wolf, $\beta $ wolf, and $\delta $ wolf active search capabilities. Dynamic weights are added to the grey wolf position update to prevent wolves from losing diversity and falling into local optimum. Through multiple benchmark function test experiments and path planning experiments, the experimental results show that the improved grey wolf optimization algorithm can effectively improve the accuracy and convergence speed, and the optimization effect is better.

Keywords

• Grey wolf algorithm
• lion optimizer algorithm
• disturbance factors
• dynamic weights
• path planning