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The Most Valuable Player Algorithm And Its Application

Posted on:2020-11-16Degree:MasterType:Thesis
Country:ChinaCandidate:X LiuFull Text:PDF
GTID:2428330572979173Subject:Computer application technology
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The Most Valuable Player Algorithm(MVPA)is a novel type of heuristic optimization algorithm designed to simulate athletes composing sports teams to compete.The algorithm improves the individual fitness value by interacting with individual competition and group competition among athletes.Get the optimal solution to the optimization problem.The researcher found that the algorithm has the advantages of easy operation,less control parameters and better stability,but it also has problems of slow convergence and late search delay.Robot path planning is the focus of robot navigation research and is needed for robot navigation.The primary problem to be solved is that it has a certain practical significance if the improved MVPA algorithm is used to solve the robot path problem.The main research results of this paper are as follows:(1)Propose the most valuable player algorithm based on mixed elite strategy and simplex strategy.The elite reverse strategy improves the mining ability of the algorithm,and the simplex strategy is used to enhance the local search ability.The experimental results show that the improved algorithm converges quickly and is easy to fall into local optimum,and the improved algorithm is applied to solve the function optimization problem.(2)Propose a two-stage training most valuable player algorithm for mixed teaching and learning algorithms.The improved most valuable player algorithm is used to solve the function optimization problem.(3)Applying the improved most valuable player algorithm to solve the robot path planning problem,constructing the obstacle material point map by Voronoi diagram,evaluating the path with four optimization algorithms,and setting different obstacles in the unit space to compare each algorithm.Time cost required...
Keywords/Search Tags:Most Valuable Player Algorithms, Elite Opposition-based Learning, Simplex Strategy Mixed, Hybrid Teaching and Learning Algorithms, Function Optimization, Engineering Optimization, Robot Path Planning
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