Little Whale Optimization Algorithm

With the rapid development of computer science and artificial intelligence, swarm intelligence optimization algorithms have been widely applied in fields such as engineering optimization, data mining, machine learning, and combinatorial optimization. Classical optimization algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization (WOA) have good search capabilities, but they still suffer from problems such as being prone to getting trapped in local optima, slow convergence speed, and difficulty in balancing exploration and exploitation. This paper proposes a novel swarm intelligence optimization algorithm—the Little Whale Optimization Algorithm (LWOA). This algorithm draws inspiration from the predation behavior, group cooperation, and energy regulation mechanisms of minke whales, combining mathematical optimization ideas and adaptive mechanisms to achieve an effective balance between global search and local exploitation. The algorithm improves optimization performance through mechanisms such as multi-level energy models, nonlinear step size adaptation, dynamic adjustment of policy probabilities, cooperative predation evolution, and a global memory bank. This paper presents the mathematical modeling, formula derivation, pseudocode implementation, and algorithm characteristic analysis of the algorithm, providing new ideas for the theoretical research and practical application of swarm intelligence optimization algorithms.