Tactical unit algorithm: A novel metaheuristic algorithm for optimal loading distribution of chillers in energy optimization

Abstract

Metaheuristic algorithms have gained increasing popularity in practical problems of engineering optimization. Aim to address the optimal chiller loading (OCL) problem in parallel chiller systems and other energy system optimization problems more efficiently, with the aim of reducing energy consumption and carbon emissions, this paper proposes a novel metaheuristic algorithm named the Tactical Unit Algorithm (TUA). The search process of this algorithm can be divided into three stages: searchers action search phase, executors action execution phase, and assessors action evaluation phase. In this paper, we use MATLAB to conduct simulation tests of benchmark functions and OCL problems. To evaluate the performance of TUA, we conduct experiments using 24 benchmark functions and compare the results with those obtained from nine commonly used metaheuristic algorithms. The findings demonstrate that TUA exhibits more accurate search accuracy, faster convergence, and better stability. Furthermore, we apply TUA to optimal chillers loading in energy optimization, and the results of three classic case tests provide preliminary evidence of the feasibility of TUA in addressing the OCL problem.