Deep-Sea Eel Optimization Algorithm

To address the common problems in complex continuous optimization problems, such as premature convergence, susceptibility to local optima, and difficulty in balancing global exploration and local exploitation, this paper proposes a novel swarm intelligence optimization method—the deep-sea eel multimodal sensing optimization algorithm. Inspired by the multi-sensory search behavior of deep-sea eels in extreme environments, this algorithm constructs a multimodal sensing search mechanism, a memory-driven waveform evolution mechanism, and a deep-sea terrain trap avoidance mechanism to systematically model the search direction, trajectory morphology, and local optimum escape process. During position updates, the algorithm incorporates multi-source information fusion, including electroinduction, pressure gradient, and terrain echo, and enhances the continuity and experience inheritance of the search process through the memory and evolution of historical successful waveforms. Simultaneously, by jointly characterizing the stability of fitness changes and the shrinking characteristics of the population structure, it explicitly identifies local optimum traps and designs a deep-sea escape dynamic based on population center and waveform memory. Theoretical analysis shows that this algorithm significantly enhances global exploration capabilities and resistance to local optima while maintaining search stability, making it suitable for high-dimensional, nonlinear, and multimodal optimization problems.