Optimization of HVAC System Using Adaptive Genetic Swarm Algorithm

In this paper a new approach is proposed for global energy consumption minimization of heating, ventilating and air conditioning (HVAC) systems. The objective function of global optimization and constraints is formulated based on mathematical models of the major components. A Genetically Swarm Optimization (GSO) algorithm is applied for energy minimization problem which is considered as a new application for GSO. The GSO algorithm combines the standard velocity and updated rules of the Particle Swarm Optimization (PSO) with the ideas of selection and mutation of the Genetic Algorithm (GA). In addition of solving the problem using GSO, a new adaptive mutation operator is presented which actively disperses the population preventing premature convergence. The adaptive genetic swarm optimization (AGSO) algorithm is applied for HVAC energy minimization problem. The results have been compared to the standard GA, adaptive GA, PSO, and GSO models. Results obtained showed that AGSO algorithm is faster in convergence and the obtained solutions have higher average fitness than other techniques.