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Research On Multi-objective Marine Predator Algorithm And Its Application

Posted on:2022-12-04Degree:MasterType:Thesis
Country:ChinaCandidate:K Y ZhongFull Text:PDF
GTID:2518306764483804Subject:Automation Technology
Abstract/Summary:PDF Full Text Request
Marine Predator Algorithm(MPA)is a meta-heuristic optimization algorithm to simulate predators' predation behavior in marine ecosystem.The Algorithm has the characteristics of high search efficiency,fast running speed,simple structure and easy implementation.Since MPA algorithm was put forward,it has been widely used in science,engineering,economy and other fields.With the deepening of the research,researchers found that the algorithm has many defects,such as low convergence accuracy,easy to trap into local optimal,suitable for dealing with single objective optimization problems.In this thesis,the shortcomings of the marine predator algorithm are analyzed and improved to improve the performance of the algorithm and expand its application range.The main work of this thesis is as follows:(1)To improve the convergence accuracy of the marine predator algorithm,a teaching-learning-based marine predator algorithm(TLMPA)was proposed by integrating the teaching-learning optimization mechanism into the marine predator algorithm.In order to evaluate the performance of TLMPA algorithm,TLMPA and seven metaheuristic optimization algorithms were analyzed and compared in the IEEE CEC-2017 benchmark functions and four engineering optimization problems.Experimental results show that TLMPA algorithm has strong competitiveness in functional optimization and engineering optimization problems.(2)To solve the multi-objective optimization problem,a multi-objective marine predator algorithm(MOMPA)was proposed.Based on Pareto dominance concept,the algorithm introduces archiving component,which enables MPA to deal with multiple object conflicts.In addition,a top predator selection mechanism based on elite selection strategy was proposed to select the best top predator from the archive to guide the foraging activities of other predators.Finally,MOMPA is applied to CEC2019 multi-modal multi-objective test set and five multi-objective engineering design problems,and compared with the performance of nine latest multi-objective optimization algorithms,experimental results show that MOMPA exhibits superior search performance and can obtain high-quality solutions.(3)As an application,the thesis applies the proposed multi-objective marine predator algorithm(MOMPA)to wind farm layout optimization design problem.Through the performance analysis and comparison between MOMPA and eight mainstream multi-objective optimization algorithms for wind farm layout optimization in different scenarios considering three optimization objectives,the results show that the success rate of MOMPA in all scenarios can reach 100%,and its search performance is superior to other multi-objective meta-heuristic optimization algorithms.
Keywords/Search Tags:Marine predator algorithm, Teaching-learning-based optimization mechanism, Pareto optimal solution, Multi-objective optimization, Engineering design problem, Wind farm layout optimization, Metaheuristic optimization algorithm
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