Multi-objective Frequency Conversion Sinusoidal Chaotic Neural Network

For the defect of Frequency Conversion Sinusoidal Chaotic Neural Network (FCSCNN) cannot solve the multi-objective optimization problem. By analyzing the action mechanism and design principle of the existing multi-objective intelligent optimization algorithm, this paper designs a multi-objective optimization algorithm model (MOFCSCNN) suitable for FCSCNN from three aspects, namely, the selection and update of global optimal solution, extraction of non-dominant solution and management of archives. Through experiments on ZDT series benchmark functions, the feasibility and effectiveness of the algorithm are verified. In order to further test the performance of the algorithm, it is compared with several commonly used multi-objective optimization algorithms. According to the simulation results, MOFCSCNN algorithm can realize the optimization of multi-objective problems, and shows better performance in convergence and optimization speed than several existing algorithms.