Diamond-Shaped Area Mobile Robot Path Planning Using Hybrid Crayfish Optimization Algorithm and Grey Wolf Algorithm

Abstract

The Evolutionary Algorithms for solving common problems in mobile robot path planning, such as Ant Colony Optimization and Genetic Algorithm, are often used. In this paper, the Crayfish optimization Algorithm is introduced and implemented to solve mobile robot path planning problems. However, it is noted that the Crayfish optimization algorithm (COA) algorithm, which is the optimization algorithm used in the Crayfish optimization Algorithm, suffers from issues such as blind search, weak solving capability, and insufficient population diversity. To address these issues and improve the coverage of the search space and the quality of the solutions in the Crayfish optimization Algorithm, a hybrid approach combining the Grey Wolf Algorithm with the Crayfish optimization Algorithm within a diamond-shaped region is proposed. During the population initialization process, a novel diamond-shaped selection method is introduced to enhance population diversity and mitigate the blind search problem. The guiding concept of the Grey Wolf Algorithm is integrated with the food source mechanism of the Crayfish optimization Algorithm to enhance problem-solving capabilities. Experimental results demonstrate that these improvements have enhanced various aspects of the algorithm's performance to some extent. This hybrid approach provides a new optimization perspective for Evolutionary Algorithms in solving mobile robot path planning problems, offering new insights into exploring Evolutionary Algorithms for handling complex problems.