Enhancing the Zebra Optimization Algorithm with Chaotic Sinusoidal Map for Versatile Optimization

Abstract

In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA)
is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibits
great optimization capabilities, but the addition of CSM increases its potential even more. This addition greatly
strengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks.
CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions,
including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM to
investigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shown
by CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatility
through multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOA
becomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficult
optimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is a
relatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership and
following, in which members of the population—zebras in this case—cooperate to solve optimisation issues in the
best possible ways