West African Lion Optimization Algorithm

Swarm intelligence optimization algorithms, by simulating the collaborative behavior of biological groups in nature, exhibit excellent global search capabilities in complex optimization problems. However, many existing biomimetic optimization algorithms suffer from high homogeneity in their structural design, mainly manifested in a single information sharing mechanism, a high dependence of search behavior on the number of iterations, and a tendency to premature convergence in high-dimensional and multimodal problems. To address these shortcomings, this paper proposes a swarm intelligence optimization algorithm based on the ecological behavioral characteristics of West African lions. Inspired by the cooperative hunting, territorial competition, asymmetric information propagation, and seasonal migration behaviors exhibited by West African lion prides in resource-scarce environments, this algorithm constructs a novel optimization framework by introducing ecological pressure functions, population memory mechanisms, asymmetric information propagation strategies, territorial fragmentation and reorganization mechanisms, and a continuous-discrete dual-mode search strategy. This algorithm can adaptively adjust the exploration and development intensity according to the population state, effectively improving global search capabilities and suppressing premature convergence. Theoretical analysis shows that this algorithm has significant advantages in maintaining search diversity and convergence stability, providing a new solution approach for complex continuous and discrete optimization problems.