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The optimization process is analyzed and summarized from the principles and characteristics of the two algorithms, A* algorithm is mainly optimized in terms of point selection and improvement of heuristic function; and ACO is mainly investigated in terms of transfer probability and pheromone positive feedback for improvement and optimization. Taking a single algorithm solving complex optimization problems difficulties into consideration, a splitting strategy can be used. So that local path or intelligent path optimization algorithms are incorporated in global path planning to improve search efficiency and optimization quality.<\/jats:p>","DOI":"10.1142\/s0218001423510060","type":"journal-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T02:09:11Z","timestamp":1675130951000},"source":"Crossref","is-referenced-by-count":19,"title":["Characteristics and Optimization Strategies of A* Algorithm and Ant Colony Optimization in Global Path Planning Algorithm"],"prefix":"10.1142","volume":"37","author":[{"given":"Yun","family":"Ni","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Jinhua Polytechnic, Jinhua 321017, P. R. China"}]},{"given":"Qinghua","family":"Zhuo","sequence":"additional","affiliation":[{"name":"College of Engineering, Zhejiang Normal University, Jinhua 321000, P. R. 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