What is it about?
An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems, which focuses on the number of search directions.
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Why is it important?
Our finding shows that the improved 32 search directions allows for a wider range of choices and a larger search range during the path search process, leading to improved exploration capabilities; and the proposed area-based heuristic information calculation, adaptive update strategy for pheromone volatile factor makes the proposed algorithm reach to a better adaptability and performance in different scenarios.
Perspectives
We hope that our results can be applied to practical scenarios and have an impact on subsequent work.
Associate Prof Na Geng
Jiangsu Normal University
Read the Original
This page is a summary of: Whether search directions number affects the efficiency of the path planning algorithm: Taking an improved ACO algorithm with 32 directions for example, Journal of Intelligent & Fuzzy Systems, April 2024, IOS Press,
DOI: 10.3233/jifs-238095.
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