Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption

Abstract

In this study, an improved invasive weed optimization (EIWO) algorithm is investigated to solve the optimal chiller loading (OCL) problem for minimization of the power consumption. In the proposed algorithm, several components are developed, such as decimal-based representation, reproduction approach, spatial dispersal method, and competitive selection mechanism. Then, the local search strategy for elite weed is proposed, which can improve the searching ability of the algorithm. To verify the efficiency and effectiveness of the proposed algorithm, three well-known instances based on the OCL problem in air-conditioning systems are tested with the comparison with other recently published algorithms. The experimental results show that the EIWO algorithm can find equal or better optimal solution compared with other algorithms. The convergence ability, stability and robustness are also verified after the detailed comparisons.