The hybrid average subtraction and standard deviation based optimizer. (February 2023) - S M, Sivalingam; Kumar, Pushpendra; Govindaraj, V.
Abstract: In this paper, we propose a new metaheuristic algorithm named the Hybrid Average Subtraction and Standard Deviation based Optimizer (HASSO) to solve the optimization problem. The proposed algorithm uses the amount of information gathered from averages, subtraction, standard deviation, and hybrid population members to explore the search space in order to reach the near-optimal or quasi-optimal solution. The mathematical modeling of the proposed algorithm has been presented in detail. We solve twenty-three well-known important functions containing the unimodal and multimodal functions to obtain their quasi-optimal solution. The results obtained for unimodal and multimodal functions indicate the high exploitation ability of HASSO in converging towards the global optima. The convergence plot of the algorithm shows that the use of five phases in the algorithm makes the algorithm tend towards the optimal or quasi-optimal solution in fewer iterations. In addition to this, the obtained results are compared with nine classical algorithms, and the sensitivity of the algorithm to the population size and number of iterations is also evaluated. Our algorithm is also statistically analyzed for superiority over other algorithms. From this analysis, HASSO's superiority over the other nine classical algorithms in providing a highly accurate solution with a lesser number of iterations is clearly shown. Highlights: A Hybrid Average Subtraction and Standard Deviation based Optimizer is proposed. The proposed algorithm uses various statistical features to explore the search space. The proposed algorithm is tested on the standard 23 benchmark functions. The results are compared with other algorithms and the sensitivity is checked. The superiority of the algorithm over the other classical algorithms is verified.