An Improved Horned Lizard Optimization Algorithm

The Horned Lizard Optimization Algorithm (HLOA) is widely applied in the field of solving optimization problems. However, this algorithm has the drawback of being prone to falling into local optimal solutions. Moreover, due to its random selection of position update strategies, the exploration ability of the population particles is reduced. Aiming at these problems, this paper proposes an Improved Horned Lizard Optimization Algorithm (IHLOA). Firstly, the Kent chaotic mapping is introduced in the particle initialization stage to make the initial distribution of particles more uniform. Secondly, the Cauchy mutation criterion is incorporated into the iterative process. By adding perturbations, the exploration ability of the algorithm is enhanced, thus further reducing the possibility of falling into local optimal solutions. Subsequently, a fitness determination criterion is proposed. Based on this criterion, different position update strategies are determined and selected, effectively improving the iterative speed of the algorithm. Finally, standard test functions are used to test and compare the IHLOA with five other algorithms. The experimental results effectively prove that the IHLOA algorithm has significantly improved both in terms of optimization seeking ability and iterative speed.