Modification of coot optimization algorithm (COA) with adaptive sigmoid increasing inertia weight for global optimization

Abstract

<p>In this paper, the classical coot optimization algorithm (COA) is modified to improve its overall performance in the exploration phase by adding an adaptive sigmoid inertia weight-based method. The modified coot optimization algorithm (mCOA) was successfully assessed using 13 standard benchmark test functions, which are frequently used to evaluate metaheuristic optimization algorithms. The MATLAB software was utilized to conduct simulation tests, and the outcome was compared with the performance of the original COA, the particle swarm optimization, and the genetic algorithm reported in the literature. The findings showed that the proposed algorithm outperformed the other algorithms on ten (10) of the 13 benchmark functions, while it maintained a competitive performance on the remaining three benchmark test functions. This indicates that mCOA provides a significant improvement to the original COA, thus making it suitable for resolving optimization problems in diverse fields. As a result, the proposed algorithm is recommended for adoption to solve real-life engineering optimization problems.</p>