Improved Partial Reinforcement Optimization Algorithm for Solving Nonlinear Equations and Application
In view of the problems of low solution accuracy,incomplete number of solutions,and slow convergence speed in solving nonlinear equations,an Improved Partial Reinforcement Optimization Algorithm that integrates improved Circle chaos mapping,Cauchy mutation,and tangent flight operator is proposed for solving equation set.An improved Circle chaos sequence is first added when initializing the population to increase the complexity of the population.A Cauchy mutation perturbation strategy is then used for the current optimal solution to improve the probability of the algorithm jumping out of the local optimum.Finally,a tangent flight operator is added in the stimulation stage to improve the needs of global exploration and local development of algorithms in different periods.Through the testing of 6 standard test functions and the solution of 8 sets of multi-root nonlinear equations,the experimental results show that the improved algorithm is superior to other algorithms in terms of robustness,optimization accuracy and convergence.The algorithm is used to solve trigonometric transcendental equations in engineering and also achieves good results.
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