Eurasian Lynx Optimization Algorithm

Swarm intelligence optimization algorithms are a class of optimization methods based on the group behavior of animals in nature, and have been widely used in engineering optimization, combinatorial problem solving, and multimodal function optimization in recent years. This paper proposes a novel swarm intelligence algorithm—the Eurasian Lynx Optimization Algorithm (ELOA), inspired by the hunting behavior and group cooperation mechanism of the Eurasian lynx. The algorithm achieves an organic combination of global search and local exploration through multi-sensory fusion, hierarchical memory guidance, adaptive energy regulation, nonlinear jump strategy, and multi-scale domain cooperation mechanism. This paper provides a detailed theoretical derivation of the algorithm modeling, iterative update formula, and group cooperation mechanism, and systematically analyzes the algorithm's characteristics. The results show that ELOA can quickly converge to the global optimum while maintaining global exploration capabilities, and exhibits good adaptability and convergence stability for multimodal problems.