Randomized Balanced Grey Wolf Optimizer (RBGWO) for solving real life optimization problems

Abstract

Grey Wolf Optimizer (GWO) is one of the most important Swarm Intelligence based meta-heuristic algorithm which follows leadership mechanism and well planned hunting strategies among wolves’ hierarchy. This paper has introduced a new variant of GWO termed as Randomized Balanced Grey Wolf Optimizer (RBGWO), which assists wolves to explore the search space in an efficient manner. The proposed algorithm improves the overall efficiency of the search process by establishing a balance between its exploitation and exploration capability incorporating three successive enhancement strategies equipped with social hierarchy mechanism and random walk with student’s t-distributed random numbers. This newly proposed variant RBGWO has outperformed GWO and its other variants (RW-GWO, EGWO+ and EGWO∗) in most of the cases on CEC 2014 benchmark functions with different scales. Results of the proposed variant have also been verified with the other meta-heuristic algorithms like GSA, CS, TPHS, CL-PSO, LX-BBO, B-BBO, SOS, DERand1Bin, Firefly, GWO, RW-GWO, EGWO+ and EGWO∗ on CEC 2014 benchmark functions. The statistical analysis of the results presents the efficiency of RBGWO (the proposed version) in overall performance. The state-of-the-art methods and the proposed algorithm have also been applied together to constrained and unconstrained real life problems. The results produced by the proposed variant are of better quality compared to that of others in these real-life problems also.