A proposed Hyper-Heuristic optimizer Nesting Grey Wolf Optimizer and COOT Algorithm for Multilevel Task

Abstract

It can be extremely difficult to find the optimal solution in many complex optimization problems. The goal of optimization algorithms in such cases is to locate a feasible solution that is as close as possible to the optimal one. These algorithms are called metaheuristic optimization algorithms and the majority of them take their inspiration from nature and work to solve challenging problems in a variety of fields. In this paper, a combination between GWO and Coot algorithm was proposed. The effectiveness of the GWO algorithm has been demonstrated in many fields, including engineering and medicine. However, GWO has a disadvantage: the potential to enter the local minima due to a lack of diversity. GWO and the Coot algorithm were merged to fix this flaw. Ten benchmark functions were used to evaluate the performance of this hybrid technique, and its results were compared to those of other common optimization algorithms, including GWO, Cuckoo Search (CS), and the Shuffled Frog Leaping algorithm (SFLA). The results show that the suggested algorithm can provide results that are both competitive and more consistent than the other algorithms in most test functions.

Network performance oriented study of sensor nodes combining improved COOT and MOAHA algorithms

“…Each bird in the flock updates its position, i.e., solution, based on its own experience and that of other birds. This updating consists of two ways, one is to find a better solution based on its own experience and the other is to improve its own solution by learning from the experience of other birds [6]. However, the COOT algorithm has a significant limitation in that it is highly susceptible to falling into a local optimum, which limits its global search capability [7].…”