IEGQO-AOA: Information-Exchanged Gaussian Arithmetic Optimization Algorithm with Quasi-opposition learning

Abstract

Arithmetic optimization algorithm (AOA) is a math optimizer proposed to solve optimization challenges. Its capability to find the global solution comes from the behavior of four arithmetic operators: multiplication, division, subtraction and addition. Local minima stagnation and sluggish convergence are the major concerns of AOA. To handle these issues, three effective modifications are proposed. Information exchange is introduced among the search agents first. Then, promising solutions around the best and current solutions are visited by a plausible way based on the Gaussian distribution. Finally, quasi-opposition of the best solution is obtained to have a higher chance of approaching the global solution. The proposed approach is named as Information-Exchanged Gaussian AOA with Quasi-Opposition learning (IEGQO-AOA). 23 standard benchmark functions, 10 CEC2020 test functions and 1 real-life engineering design problem are solved by the proposed IEGQO-AOA and its competing peers such as the original and modified versions of AOA, dwarf mongoose optimization, reptile search algorithm, aquila optimizer, bat algorithm, sine cosine algorithm, original and enhanced version of salp swarm algorithm, dragonfly search algorithm, LSHADE-EpSin, stochastic fractal search, improved jaya and moth-flame optimization, perturbed stochastic fractal search and nelder-mead simplex orthogonal learning moth-flame optimization algorithm. Comparative results based on the statistical tests ratify the potential of IEGQO-AOA in solving problems concerning accuracy and convergence without compromising on the algorithm’s simplicity much.