In response to the growing need for adaptive optimization algorithms capable of handling complex, multimodal, and high-dimensional search spaces, this paper introduces the Structured Random Cycle-guided Algorithm (SRCA). SRCA is not presented as a fundamentally new optimization paradigm, but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection. Based on a cycle-structured architecture, directional and stochastic search behaviors are dynamically selected at the individual level. The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies, enabling a coherent and adaptive balance between exploration and exploitation throughout the optimization process. Unlike traditional metaheuristics that rely on fixed behavioral roles or static movement schemes, SRCA allows each individual to adapt its search strategy based on real-time population feedback, monitored through convergence and dispersion indicators. The performance of SRCA is quantitatively assessed under strictly identical experimental conditions on a comprehensive set of 23 benchmark functions, including multimodal and high-dimensional problems, as well as on six classical constrained engineering design problems. Numerical results demonstrate competitive convergence reliability and robustness across diverse optimization tasks, confirming the effectiveness of the proposed adaptive cycle-based framework.
Marannano et al. (Thu,) studied this question.