Opposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling for global optimization

Moth flame optimization (MFO) algorithm proves to be an excellent choice for numerical optimization. However, for some complex objectives, MFO may get trapped in local optima or suffer from premature convergence. In order to overcome these issues, an improved MFO-based algorithm, called opposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling (OMFO), is presented. The proposed method integrates opposition-based learning (OBL) with Cauchy mutation (CM) and evolution boundary constraint handling (EBCH) technique with MFO to improve its performance. OBL and EBCH improve the convergence of MFO, while CM helps MFO to escape local optima. The effect of each method (OBL, CM, EBCH) on MFO is validated using 18 benchmark functions and two constrained real-world problems. Simulation results indicate that opposition-based MFO integrated with Cauchy mutation and EBCH has the best performance among the MFO variants. The OMFO algorithm is also compared with various algorithms in the literature and provides competitive results in terms of increased exploitation and exploration capability, improved convergence and local optima avoidance.