Hybrid Mutation Pathfinder Algorithm Embedded with Cat Mapping and Its Application
A hybrid mutation pathfinder algorithm embedded with Cat mapping(CHMPFA)is proposed for the function optimization problem in view of the problems of low accuracy of pathfinder algorithm(PFA)solution,slow speed of finding the best and easy to fall into local optimum.Firstly,using the characteristics of Cat chaotic mapping such as randomness and dispersion,combined with the guiding effect of opposition-based learning,the population can cover a wider search space and improve the global search capability of the algorithm.Secondly,the introduction of reduction factors in the pathfinder position update phase balances the global and local search ca-pabilities of the algorithm,gradually narrowing the search space range through the growth of the number of iterations,helping the algorithm to find the optimal solution quickly,thus enhancing the search speed and convergence of the algorithm.In the end,the optimal individual is perturbed in position using the mutation probability of randomly selected Cauchy mutation or Gaussian mutation,and the two mutation strategies can help individual quickly jump out of the local optimum to other regions.The CHMPFA is tested on 10 classical benchmark test functions and 12 complex CEC2017 function,and applied to pressure vessel engineering design problem.The experimental results are compared with those of the original algorithm and other algorithms,and the results show that the CHMPFA is sig-nificantly enhanced in terms of solution accuracy,finding speed and local optimum avoidance,and the lower engineering cost further validate the robustness of the CHMPFA.
pathfinder algorithmfunction optimization problemCat maphybrid mutationengineering optimization problem
万方数据
维普
