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The optimal chiller loading ( OCL ) is one of the most essential issues for saving energy and costs. Because of the pervasive use of chiller systems in the world, even saving a small amount of energy consumption in a chiller system can be significant. This study introduces a hybrid algorithm that could achieve similar or better results than those presented in previous studies for OCL problem. The whale optimization algorithm (WOA) is a recent promising algorithm for solving optimization problems with the small number of tuning parameters. However, it suffers from its inefficient exploitation around the best solution. To address this shortcoming, WOA-SQP was introduced that uses a  sequential quadratic programming  (SQP) method to exploit efficiently the search space around the best solution obtained by WOA. Although, WOA-SQP could achieve good results, but the variance of its solutions is high for different runs on the same problem. The non-deterministic distributed parallel framework of the population P system (PPS) enhances the diversity of WOA-SQP to minimize the variance of the solutions that shows the stability of the algorithm. Simulation results of the proposed algorithm on several case studies show the better performance of the proposed algorithm in comparison with the recent approaches.

Optimal chiller loading for energy conservation using a hybrid whale optimization algorithm based on population membrane systems