CQND-WHO: chaotic quantum nonlinear differential wild horse optimizer

Wild horse optimizer (WHO) features a simple structure with few parameters, facilitating fast convergence. However, it suffers from low global search efficiency and struggles to escape local optima during late-stage re-evolution. To address WHO’s underutilization of individuals within the population, a quantum-accelerated search algorithm was developed to enhance individual search capabilities. Moreover, a global perturbation strategy based on chaotic mapping was introduced to overcome the challenge of escaping local optima. To improve the algorithm's search efficiency, an adaptive evolution strategy was formulated. Finally, introduce the novel hybrid optimization algorithm, named chaotic quantum nonlinear differential wild horse optimizer (CQND-WHO). In this study, we tested CQND-WHO using 23 classical test suites, the CEC2014 and CEC2017 benchmarks. The results demonstrate that the improved strategy effectively addresses WHO's shortcomings. Furthermore, when compared to other optimization algorithms considered in this study, CQND-WHO exhibits significantly enhanced convergence accuracy and efficiency in solving high-dimensional complex multi-peak problems, offering a novel approach to addressing multidimensional complex multi-peak problems.