Mantis optimization algorithm based on non-dominated sorting
Problems in the real society are often not only a single goal but a complex multi-objective problem.Solving such problems requires an efficient optimization algorithm.Based on the mantis optimization algorithm,a new mantis optimization algorithm(NSDBO)is proposed to solve the multi-objective optimization problem.Based on the non-dominant ranking method in NSGA-Ⅱ algorithm,the traditional external archiving strategy is replaced with dynamic external archiving,and a new crowding distance formula is proposed to enhance the global optimization ability of the algorithm and maintain the diversity of the population.In order to verify the efficiency and effectiveness of NSDBO algorithm,ZDT series and DTLZ series test functions were selected to test it,and the results show that the convergence accuracy is improved by 21.5％～44.3％.It is applied to the fault diagnosis of transformer,and the results show that ND-SVM has higher accuracy than SVM and meets the standard of engineering application.
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