Optimal chiller loading by improved parallel particle swarm optimization algorithm for reducing energy consumption

Abstract

Aimed at the optimal chiller loading problem in parallel chillers system, an improved parallel particle swarm optimization (IPPSO) algorithm is proposed. This algorithm uses random and chaotic sequence mechanisms to initialize particles respectively, so that the two populations have different characteristics at the beginning of generation. Meanwhile, a new immigration operator is proposed to break the internal balance of the population, enhance the diversity of the population and promote the population evolve to a higher level. Besides, according to the characteristics of the two populations, different improvement strategies for inertia weight are adopted to accelerate the convergence speed of the algorithm further. Finally, the performance of the proposed IPPSO algorithm is tested with two well-known parallel chillers system cases, and its experimental results are compared with other algorithms. The experimental results show that compared with other algorithms, the IPPSO algorithm can find the better optimal solution and present an obvious energy-saving effect. The convergence ability, computational complexity and robustness are also verified after the detailed comparisons.