A novel metaheuristic inspired by horned lizard defense tactics

Abstract

This paper introduces HLOA, a novel metaheuristic optimization algorithm that mathematically mimics crypsis, skin darkening or lightening, blood-squirting, and move-to-escape defense methods. In crypsis behavior, the lizard changes its color by becoming translucent to avoid detection by its predators. The horned lizard can lighten or darken its skin, depending on whether or not it needs to decrease or increase its solar thermal gain. The skin darkening or lightening strategy is modeled by including the stimulating hormone melanophore rate( α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}-MHS) that influences these skin color changes. Further, the move-to-evasion strategy is also mathematically described. The horned lizard’s shooting blood defense mechanism, described as a projectile motion, is also modeled. These strategies balance exploitation and exploration mechanisms for local and global search over the solution space. HLOA performance is benchmarked with sixty-three optimization problems from the literature, testbench problems provided in IEEE CEC- 2017 “Constrained Real-Parameter Optimization”, analyzed for dimensions 10, 30, 50, and 100, as well as testbench functions from IEEE CEC-06 2019 “100-Digit Challenge”. Moreover, three real-world constraint optimization applications from IEEE CEC2020 and two engineering problems, the multiple gravity assist optimization and the optimal power flow problem, are also studied. Wilcoxon and Friedman statistics tests compare the HLOA algorithm results against ten recent bio-inspired algorithms. Wilcoxon shows that HLOA provides the optimal solution for most testbench functions more effectively than competing algorithms. At the same time, the Friedman statistics test ranks the HLOA first, and the n-dimensional analysis shows that it performs better on the constrained optimization problems for dimensions 50 and 100. The source code is free and available from https://www.mathworks.com/matlabcentral/fileexchange/159658-horned-lizard-optimization-algorithm-hloa.