Weighted Mean Variant with Exponential Decay Function of Grey Wolf Optimizer on Applications of Classification and Function Approximation Dataset

摘要

Nature-Inspired Meta-heuristic algorithms are optimization algorithms those are becoming famous day by day from last two decades for the researcher with many key features like diversity, simplicity, proper balance between exploration and exploitation, high convergence rate, avoidance of stagnation, flexibility, etc. There are many types of nature inspired meta-heuristics algorithms employed in many different research areas in order to solve complex type of problems that either single-objective or multi-objective in nature. Grey Wolf Optimizer (GWO) is one most powerful, latest and famous meta-heuristic algorithm which mimics the leadership hierarchy which is the unique property that differentiates it from other algorithms and follows the hunting behavior of grey wolves that found in Eurasia and North America. To implement the simulation, alpha, beta, delta, and omega are four levels in the hierarchy and alpha is most powerful and leader of the group, so forth respectively. No algorithm is perfect and hundred percent appropriate, i.e. replacement, addition and elimination are required to improve the performance of each and every algorithm. So, this work proposed a new variant of GWO namely, Weighted Mean GWO (WMGWO) with an exponential decay function to improve the performance of standard GWO and their many variants. The performance analysis of proposed variant is evaluated by standard benchmark functions. In addition, the proposed variant has been applied on Classification Datasets and Function Approximation Datasets. The obtained results are best in most of the cases.