Bactrian Camel Inspired Optimization Algorithm

This paper proposes a novel optimization algorithm inspired by the physiological characteristics and group behavior of Bactrian camels. This algorithm achieves an adaptive balance between local search and global hopping by incorporating a bimodal energy mechanism, an environmental perception mechanism, a migration path memory mechanism, and a dynamic energy regulation mechanism. The algorithm's mathematical model is described in plain text, detailing individual initialization, energy consumption, position updates, environmental perception regulation, and migration path memory mechanism. Theoretically, this algorithm can improve the convergence efficiency and global search capabilities of complex optimization problems and demonstrates the adaptive strategies of Bactrian camels to extreme conditions in their natural environment.