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Research On Optimization Algorithm And Application Of Teaching And Learning Pathfinder

Posted on:2022-01-17Degree:MasterType:Thesis
Country:ChinaCandidate:C M TangFull Text:PDF
GTID:2518306488971819Subject:Computer application technology
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Pathfinder algorithm(PFA)is a heuristic optimization algorithm that imitates the behavior of animal group leaders to find the best food area or prey.The PFA algorithm has the characteristics of simple and clear structure and strong search ability.However,the research found that the algorithm still has the disadvantages of low solution accuracy,slow convergence speed and easy to fall into local minima(large)when solving optimization problems.This paper analyzes and improves the shortcomings of the Pathfinder optimization algorithm,and proposes two improved Pathfinder optimization algorithms.And the improved algorithm is applied to actual optimization problems.The purpose is to further improve the theoretical basis of the PFA algorithm and broaden its application range.The main research work of this paper is as follows:(1)In order to prevent the PFA algorithm from falling into the local optimum,and to improve the optimization accuracy and speed of the algorithm,the teaching and learning optimization algorithm and the exponential step operator are introduced,and a teaching and learning-based pathfinder optimization algorithm(Teaching and Learning,TLPFA).TLPFA is used in 19 four different types of benchmark test functions and 6 benchmark project example problems to compare and analyze different types of meta-heuristic algorithms.The experimental results show that TLPFA has strong competitiveness in function optimization and engineering examples.(2)In order to balance the exploration and mining capabilities of PFA,improve the algorithm's solution accuracy and natural mechanism,introduce acceptance and communication operators,guides and mutation mechanisms,and propose an enhanced pathfinder optimization algorithm(Improved PFA,IMPFA).IMPFA is applied to 9 benchmark engineering example problems with different structures,and the test results are compared and analyzed with other algorithms.The results show that IMPFA has strong solving ability when solving problems of this type and is superior to other algorithms.(3)The pathfinder optimization algorithm based on teaching and learning is used in the UCAV path planning problem.The problem to be solved is to plan the best or sub-optimal flight route for the UAV Pathway(UCAV)in an appropriate time.The results obtained by IMPFA to solve the UCAV problem are compared and analyzed with other 7 algorithms.The results show that the search accuracy and search speed of TLPFA in solving the actual problem are higher than those of the compared algorithm.It further proves that TLPFA is an effective and feasible algorithm.
Keywords/Search Tags:Pathfinder optimization algorithm, Teaching and learning optimization algorithm, Exponential step, Evolution operator, UCAV path planning, Heuristic optimization algorithm
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