Application of Improved Genetic Algorithms in Path Planning

Abstract

Genetic algorithm, as a heuristic optimization technique, has achieved remarkable results in various fields, including path planning. Path planning, a fundamental challenge in automation systems and robotics, involves finding the optimal route from a starting point to a destination. This paper focuses on the application of improved genetic algorithms in path planning. Firstly, a brief overview of the fundamental principles of traditional genetic algorithms is provided, including operations such as individual encoding, selection, crossover, and mutation. Subsequently, a thorough exploration is conducted into how improved genetic algorithms can be introduced to address path planning problems. These enhancements encompass optimized population initialization strategies, novel genetic operation methods, and more effective fitness function designs. Furthermore, the discussion extends to how these improvements contribute to accelerating convergence speed, enhancing global search capabilities, and elevating the quality of solution outcomes. A substantial number of objective experiments have demonstrated the effectiveness of our approach.