A novel Kangaroo Escape Optimizer

Abstract

In this study, we propose a novel metaheuristic algorithm named Kangaroo Escape optimizer, inspired by the survival-driven escape strategies of kangaroos in unpredictable environments. The algorithm integrates a chaotic logistic energy adaptation strategy to balance a two-phase exploration process—zigzag motion and long-jump escape—and an adaptive exploitation phase with local search guided by either nearby elite solutions or random peers. A unique decoy drop mechanism is introduced to prevent premature convergence and ensure dynamic diversity.