Optimization of Microgrid Scheduling Based on Adaptive Collaborative Secretary Bird Optimization Algorithm

With the continuously increasing penetration of renewable energy and the growing structural complexity of modern energy systems, the efficient and reliable solution of large-scale, high-dimensional, nonlinear, and strongly constrained optimization problems has become a critical research topic in the field of intelligent optimization. The Secretary Bird Optimization Algorithm (SBOA), a recently proposed swarm intelligence method, achieves a global search by simulating the hunting and defense behaviors of secretary birds and has demonstrated a certain potential in continuous optimization problems. However, when applied to high-dimensional, multimodal, and complex engineering optimization problems, the standard SBOA still suffers from limitations in collaborative search capabilities, late-stage convergence accuracy, and boundary-handling mechanisms. To address these shortcomings, this paper proposes an Adaptive Collaborative Secretary Bird Optimization Algorithm (ACSBOA). From a multi-strategy collaborative perspective, three key mechanisms are incorporated into the original SBOA: (1) an adaptive collaborative search strategy, which integrates positional information from the best, suboptimal, worst, and randomly selected individuals to guide the population toward more directional and efficient exploration; (2) a quadratic interpolation-based local exploitation strategy, designed to enhance fine-grained search capability during the later stages of optimization; and (3) a soft boundary pullback mechanism, which preserves solution feasibility while effectively maintaining population diversity. Through the synergistic interaction of these strategies, ACSBOA achieves a better balance of exploration ability, convergence speed, and algorithmic stability. The optimization performance of ACSBOA is systematically evaluated on the CEC2017 and CEC2022 benchmark suites across different problem dimensions and function categories. The experimental results demonstrate that ACSBOA significantly outperforms several state-of-the-art comparison algorithms in terms of solution accuracy, convergence speed, and robustness. Furthermore, ACSBOA is successfully applied to a 24 h optimal scheduling problem of a grid-connected microgrid. The simulation results indicate that the proposed algorithm can substantially reduce operational costs while satisfying all system operating constraints, thereby validating its effectiveness and practical applicability in real-world engineering optimization problems.