Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm

This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorologi...

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Vydané v:Mathematics (Basel) Ročník 10; číslo 19; s. 3466
Hlavní autori: Abdel-Basset, Mohamed, Mohamed, Reda, Sallam, Karam M., Chakrabortty, Ripon K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.10.2022
Predmet:
Algorithms
Chemistry
Design engineering
Dispersion
Ebola virus
Evolution
Evolutionary algorithms
Evolutionary computation
Foraging behavior
Heuristic methods
Heuristic programming
Light
Light Spectrum Optimizer
Mathematical optimization
metaheuristic
Mutualism
Neighborhoods
Optimization
Optimization algorithms
Physics
physics-based algorithm
Predation
Runge-Kutta method
Search algorithms
Statistical analysis
Egypt
ISSN:2227-7390, 2227-7390
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Shrnutí:This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math10193466