This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard’s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard’s retreat to the treetops after feeding. To assess FLO’s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two standard benchmark functions. These functions include unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions, as well as the challenging CEC 2017 test suite. FLO’s performance is benchmarked against twelve established metaheuristic algorithms, providing a comprehensive comparative analysis. The simulation results demonstrate that FLO excels in both exploration and exploitation, effectively balancing these two critical aspects throughout the search process. This balanced approach enables FLO to outperform several competing algorithms in numerous test cases. Additionally, FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems, further validating its robustness and versatility in solving real-world optimization challenges. Overall, the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

Reconfiguration, as well as optimal utilization of distributed generation sources and capacitor banks, are highly effective methods for reducing losses and improving the voltage profile, or in other words, the power quality in the power distribution system. Researchers have considered the use of distributed generation resources in recent years. There are numerous advantages to utilizing these resources, the most significant of which are the reduction of network losses and enhancement of voltage stability. Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Intersect Mutation Differential Evolution (IMDE) algorithms are used in this paper to perform optimal reconfiguration, simultaneous location, and capacity determination of distributed generation resources and capacitor banks. Three scenarios were used to replicate the studies. The reconfiguration of the switches, as well as the location and determination of the capacitor bank’s optimal capacity, were investigated in this scenario. However, in the third scenario, reconfiguration, and determining the location and capacity of the Distributed Generation (DG) resources and capacitor banks have been carried out simultaneously. Finally, the simulation results of these three algorithms are compared. The results indicate that the proposed NSGAII algorithm outperformed the other two multi-objective algorithms and was capable of maintaining smaller objective functions in all scenarios. Specifically, the energy losses were reduced from 211 to 51.35 kW (a 75.66% reduction), 119.13 kW (a 43.54% reduction), and 23.13 kW (an 89.04% reduction), while the voltage stability index (VSI) decreased from 6.96 to 2.105, 1.239, and 1.257, respectively, demonstrating significant improvement in the voltage profile.