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 the proposed is light dispersions with different angles while passing through rain droplets, causing meteorological phenomenon of colorful rainbow spectrum. In order to validate algorithm, three experiments are conducted. First, LSO tested on solving CEC 2005, and obtained results compared wide range well-regarded metaheuristics. second experiment, used four competitions in single objective benchmarks (CEC2014, CEC2017, CEC2020, CEC2022), its eleven well-established recently-published optimizers, named grey wolf optimizer (GWO), whale (WOA), salp swarm (SSA), evolutionary algorithms like differential evolution (DE), optimizers including gradient-based (GBO), artificial gorilla troops (GTO), Runge–Kutta method (RUN) beyond metaphor, African vultures (AVOA), equilibrium (EO), Reptile Search Algorithm (RSA), slime mold (SMA). addition, several engineering design problems solved, many from literature. experimental statistical analysis demonstrate merits highly superior performance algorithm.