<p>This paper proposes an Orthogonal Opposition-Based Learning and Combined Hyperbolic Sine-Cosine Particle Swarm Optimization (OOBL-CHSCPSO). It aims to address the challenges of premature convergence and an insufficient balance between exploration and exploitation in complex, high-dimensional optimization problems. The algorithm integrates orthogonal learning strategies, hybrid sine-cosine mechanisms, adaptive weights, quantum mutation, and a stochastic elimination strategy. These components enhance its global search ability and help avoid local optima. Across nine classical and CEC2017 benchmarks, OOBL-CHSCPSO consistently surpasses PSO and seven recent algorithms, boosting convergence speed by 48.68% to 77.91% and improving the final best objective by 40.81% to 64.90%. It also enhances the mean and standard deviation by 75.33% and 78.44% versus PSO, and by 43.92% and 52.18% versus the seven algorithms. The study applies OOBL-CHSCPSO to a CCHP-Plus system that combines solar energy, natural gas, electricity, and thermal storage to meet varied demands for power, cooling, and heating. We construct a detailed model incorporating energy prices, efficiency parameters, and emissions, and compare the algorithm’s performance with other advanced methods. On the real CCHP-Plus system, the proposed method achieves an Energy Consumption Saving Rate (ECSR) of 15%, a Cost Saving Rate (CSR) of 20%, and an Emission Reduction Rate (ERR) of 18%. It also outperforms standard PSO and recent metaheuristics in terms of solution quality and convergence speed. The proposed approach not only enhances energy efficiency and reduces emissions but also provides a scalable framework for intelligent energy management. Its flexibility and robustness make it suitable for a wide range of real-world multi-energy system applications.</p>