Modified differential evolution to solve systems of nonlinear equations

Solving systems of nonlinear equations (NESs) is a significant and challenging task in the field of sciences and engineering. This article proposes a modified differential evolution (MDE) to solve the NESs problem. Using principle of the particle swarm optimization, it employed novel mutation structure with modernistic control factors, to heighten exploration and exploitation activities. Correspondingly, a different crossover rate is presented in MDE, to enrich the convergence rapidity. Lastly, a new selection system is familiarized in MDE, to estimate diversity and evade from the local minimum. To certify the performance of MDE, thirteen typical benchmark test suites are solved. Additionally, optimize the seven cases of NESs, to prove the applicability of MDE. The experimental and comparative outcomes indicate that the suggested MDE performs more effectively on complex test functions and NESs.