Draco lizard optimizer: a novel metaheuristic algorithm for global optimization problems

This research introduces a novel meta-heuristic optimization technique, termed the Draco lizard optimizer (DLO), grounded in the distinctive behaviors exhibited by the Draco lizard, particularly its proficient gliding mechanisms and adaptive ecological strategies. The DLO algorithm ingeniously translates these survival techniques into an efficient search paradigm, aimed at resolving intricate global optimization problems. To assess the optimization efficacy of the DLO algorithm, we embarked on a comparative experimentation framework, incorporating 29 benchmark functions sourced from the prestigious CEC2017 test suite, alongside a selection of five cutting-edge metaheuristic algorithms. The analysis of the experimental outcomes highlights the advantages of the DLO algorithm, which achieved an average ranking of 1.62 on the CEC2017 benchmark set, placing it in the top position. Additionally, the average computational time of the DLO algorithm is one-third that of the second-ranked CPO algorithm, confirming that the DLO algorithm not only possesses exceptional global search performance but also demonstrates higher search efficiency compared to contemporary optimization algorithms. Source code for DLO algorithm: https://github.com/XiaoweiWang-ai/DLO