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

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...

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Vydáno v:Knowledge-based systems Ročník 260; s. 110169
Hlavní autor: Çelik, Emre
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 25.01.2023
Témata:
Arithmetic optimization algorithm
Gaussian distribution
Information exchange
Metaheuristic
Optimization
Quasi-opposition learning
Gaussian distribution
Metaheuristic
Arithmetic optimization algorithm
Quasi-opposition learning
Information exchange
Optimization
ISSN:0950-7051, 1872-7409
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Shrnutí: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. •A modified version of AOA (IEGQO-AOA) is presented to tackle optimization problems.•IEGQO-AOA originates from information exchange, adaptive Gaussian distribution and quasi-opposition learning.•The included mechanisms achieve desirable balance between exploration and exploitation.•Standard/CEC2020 benchmark functions and one practical vital problem are solved.•Despite its simplicity, IEGQO-AOA outperforms its competing peers in most problems.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.110169