Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem

Abstract

A multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy (MOCOA-ML) was proposed to solve the optimal power flow (OPF) problem. MOCOA-ML adds external archives with grid mechanism on the basis of elite non-dominated sorting. It can guarantee the diversity of the population while obtaining the Pareto solution set. When selecting elite coyotes, there is a greater probability to select the elite in sparse areas, which is conducive to the development of sparse areas. In addition, combined with Meta-Lamarckian learning strategy, based on four crossover operators (horizontal crossover operator, longitudinal crossover operator, elite crossover operator and direct crossover operator), the local search method is adaptively selected for optimization, and its convergence performance is improved. First, the simulation is carried out in 20 test functions, and compared with MODA, MOPSO, MOJAYA, NSGA-II, MOEA/D, MOAOS and MOTEO. The experimental results showed that MOCOA-ML achieved the best inverted generational distance value and the best hypervolume value in 11 and 13 test functions, respectively. Then, MOCOA-ML is used to solve the optimal power flow problem. Taking the fuel cost, power loss and total emissions as objective functions, the tests of two-objective and three-objective bechmark problems are carried out on IEEE 30-bus system and IEEE 57-bus system. The results are compared with MOPSO, MOGWO and MSSA algorithms. The experimental results of OPF demonstrate that MOCOA-ML can find competitive solutions and ranks first in six cases. It also shows that the proposed method has obtained a satisfactory uniform Pareto front.