In order to overcome the several shortcomings of Particle Swarm Optimization (PSO) e.g., premature convergence, low accuracy and poor global searching ability, a novel Simple Particle Swarm Optimization based on Random weight and Confidence term (SPSORC) is proposed in this paper. The original two improvements of the algorithm are called Simple Particle Swarm Optimization (SPSO) and Simple Particle Swarm Optimization with Confidence term (SPSOC), respectively. The former has the characteristics of more simple structure and faster convergence speed, and the latter increases particle diversity. SPSORC takes into account the advantages of both and enhances exploitation capability of algorithm. Twenty-two benchmark functions and four state-of-the-art improvement strategies are introduced so as to facilitate more fair comparison. In addition, a t-test is used to analyze the differences in large amounts of data. The stability and the search efficiency of algorithms are evaluated by comparing the success rates and the average iteration times obtained from 50-dimensional benchmark functions. The results show that the SPSO and its improved algorithms perform well comparing with several kinds of improved PSO algorithms according to both search time and computing accuracy. SPSORC, in particular, is more competent for the optimization of complex problems. In all, it has more desirable convergence, stronger stability and higher accuracy.

Traditional combined cooling, heating, and power (CCHP) systems are highly efficient in energy utilization but face challenges such as high operational costs, CO₂ emissions, and complex scheduling. In traditional CCHP systems, typically used in large commercial settings like hospitals or shopping centers, daily operational costs can exceed $1500, and CO₂ emissions often surpass 1.5 tons, limiting broader adoption. This study introduces an improved CCHP system (CCHP‐Plus), which integrates photovoltaic thermal (PV/T) technology and energy storage equipments (ESEs) to mitigate these issues. PV/T collectors generate both electricity and heat, reducing natural gas dependence, while ESE balances energy supply and demand for enhanced management. The effectiveness of CCHP‐Plus is assessed using three key indicators: primary energy consumption, operational cost, and CO₂ emissions. NSGAIII‐AC‐GM delivers a 20% reduction in operational costs and a 10% decrease in CO₂ emissions, outperforming seven other algorithms in optimization efficiency on DTLZ and IMOP problems. Furthermore, the algorithm demonstrates superior performance across four CCHP‐Plus scenarios, making it a promising solution for sustainable energy systems. These findings offer valuable numerical insights, showcasing the system's potential for real‐world applications.