| Pathfinder optimization algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the hunting and searching behavior of populations in nature.The algorithm has the characteristics of parallelization,high search efficiency,fast optimization speed,high optimization accuracy,simple structure and easy to implement.With the deepening of research,researchers have found that the algorithm is easy to fall into local optimum and the accuracy of optimization is not high,which limits its application scope.Aiming at the shortcomings of the algorithm,this paper improves it from the aspects of encoding scheme and new evolution strategy.The purpose is to balance the exploration and exploitation capabilities of the algorithm,improve its global search performance,and apply it to multi-objective optimal power flow problems of wind farm layout optimization,proton exchange membrane fuel cell parameteM extraction optimization,and integration of wind power and other stochastic renewable energy sources.The main work of this paper is as follows:(1)In order to solve the problem of low accuracy of wind Farm Layout Optimization(WFLO)problem,a named Discrete Complex Coded Pathfinder Optimization Algorithm(DCPFA)was proposed.The algorithm uses the idea of complex encoding to improve the algorithm,which further balances the exploration and exploitation ability of the algorithm.Two complex wind farm wind conditions are simulated to test the performance of DCPFA in solving the WFLO problem.The experimental results show that DCPFA is effective and robust in solving WFLO problem.(2)In order to solve the parameter optimization problem of proton exchange membrane fuel cell(PEMFC)in accurate modeling.This paper introduced quantum coding,proposed a Quantum coded Pathfinders Optimization algorithm(QPFA),and applied it to three kinds of PEMFC parameters extraction which had been commercialized.The experimental results are compared with the latest literature to show the accuracy and high precision of QPFA in extracting PEMFC parameters.(3)On the basis of Work 1 and 2,a variety of stochastic renewable energy sources such as wind power generation are introduced,and a multi-objective optimal power flow problem model for a variety of stochastic renewable energy sources is established.In order to solve the multi-optimization problem,the paper proposed a named Multi-Objective Pathfinder Optimization Algorithm(MOPFA).This algorithm is based on the concept of Pareto dominance,the archiving component is introduced,and the test system uses the modified IEEE-30-bus system.The experimental results show that MOPFA can give a more uniform solution set and provide a greener solution,and the performance of the algorithm is better than some other classical multi-objective metaheuristic optimization algorithms on this problem. |
