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Title: Improving the cooperation of fuzzy simplified memory A* search and particle swarm optimisation for path planning

Authors: Mehdi Neshat; Ali Akbar Pourahmad; Zahra Rohani

Addresses: Department of Computer Science, College of Software Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran ' Department of Information Science, College of Library Science, Shirvan Branch, Islamic Azad University, Shirvan, Iran ' Department of Computer Science, College of Hardware Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran

Abstract: Problem solving is a very important subject in the world of AI. In fact, a problem can be considered one or more goals along with a set of available interactions for reaching those goals. One of the best ways of solving AI problems is to use search methods. The simplified memory bounded A* (SMA*) is one of the best methods of informed search. In this research, a hybrid method was proposed to increase the performance of SMA* search. The combining fuzzy logic with this search method and improving it with PSO algorithm brought satisfactory results. The use of fuzzy logic leads to increase the search flexibility especially when a robot dealing with lots of barriers and path changes. Furthermore, combining PSO saves the search from being trapped into local optimums and provides for search some correct and accurate suggestions. In the proposed algorithm, the results indicate that the cost of search and branching factor are decreased in comparison with other methods.

Keywords: informed search; fuzzy logic; particle swarm optimisation; simplified memory bounded A*; robot navigation.

DOI: 10.1504/IJSI.2020.106388

International Journal of Swarm Intelligence, 2020 Vol.5 No.1, pp.1 - 21

Accepted: 22 Jul 2017
Published online: 20 Mar 2020 *

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