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( $$\alpha$$α -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.

A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.

Selective harmonics elimination (SHE) is a widely applied control strategy in multilvel inverters for harmonics reduction. SHE is designed for the elimination of low-order harmonics while keeping the fundamental component equal to any previously specified amplitude. This paper proposes a novel bio-inspired metaheuristic optimization algorithm called Black Widow Optimization Algorithm (BWOA) for solving the SHE set of equations. BWOA mimics the spiders’ different movement strategies for courtship-mating, guaranteeing the exploration and exploitation of the search space. The optimization results show the reliability of BWOA compared to the state-of-the-art metaheuristic algorithms and show competitive results as a microalgorithm, opening its future application for an on-line optimization calculation in low requirement hardware.

This paper proposes a new meta-heuristic called Jumping Spider Optimization Algorithm (JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the behavior of spiders in nature and mathematically models its hunting strategies: search, persecution, and jumping skills to get the prey. These strategies provide a fine balance between exploitation and exploration over the solution search space and solve global optimization problems. JSOA is tested with 20 well-known testbench mathematical problems taken from the literature. Further studies include the tuning of a Proportional-Integral-Derivative (PID) controller, the Selective harmonic elimination problem, and a few real-world single objective bound-constrained numerical optimization problems taken from CEC 2020. Additionally, the JSOA’s performance is tested against several well-known bio-inspired algorithms taken from the literature. The statistical results show that the proposed algorithm outperforms recent literature algorithms and is capable to solve challenging real-world problems with unknown search space.

This paper presents a novel meta-heuristic algorithm inspired by the visual capabilities of the mantis shrimp (Gonodactylus smithii), which can detect linearly and circularly polarized light signals to determine information regarding the polarized light source emitter. Inspired by these unique visual characteristics, the Mantis Shrimp Optimization Algorithm (MShOA) mathematically covers three visual strategies based on the detected signals: random navigation foraging, strike dynamics in prey engagement, and decision-making for defense or retreat from the burrow. These strategies balance exploitation and exploration procedures for local and global search over the solution space. MShOA’s performance was tested with 20 testbench functions and compared against 14 other optimization algorithms. Additionally, it was tested on 10 real-world optimization problems taken from the IEEE CEC2020 competition. Moreover, MShOA was applied to solve three studied cases related to the optimal power flow problem in an IEEE 30-bus system. Wilcoxon and Friedman’s statistical tests were performed to demonstrate that MShOA offered competitive, efficient solutions in benchmark tests and real-world applications.

This paper presents a modification to the dingo optimization algorithm (mDOA) to solve the non-linear set of equations of the selective harmonic elimination (SHE) control technique widely applied in multilevel inverters. In addition, said modification is conducted to the survival criteria by including a local search to provide a better balance when replacing vectors (dingoes) with a low survival rate. The proposed method is also benchmarked with some unimodal functions to illustrate its better exploitation capabilities. Finally, the SHE optimization results were calculated and compared with three well-known state-of-the-art metaheuristics, where the modified version of the dingo optimization algorithm showed very competitive results. The significant difference between the mDOA results and the rest of the algorithms is determined by the Wilcoxon rank-sum non-parametric statistical test with a 5% degree of significance. The p-values confirm the meaningful advantage of the mDOA compared to other bio-inspired algorithms for many modulation indexes. Experimentally, the proposed algorithm is validated through the implementation of a three-phase 11-level inverter.