Enhanced parallel salp swarm algorithm based on Taguchi method for application in the heatless combined cooling-power system

Salp swarm algorithm (SSA) is an excellent meta-heuristic algorithm, which has been widely used in the engineering field. However, there is still room for improvement in terms of convergence rate and solution accuracy. Therefore, this paper proposes an enhanced parallel salp swarm algorithm based on the Taguchi method (PTSSA). The parallel trick is to split the initial population uniformly into several subgroups and then exchange information among the subgroups after a fixed number of iterations, which speeds up the convergence. Communication strategies are an important component of parallelism techniques. The Taguchi method is widely used in the industry for optimizing product and process conditions. In this paper, the Taguchi method is adopted into the parallelization technique as a novel communication strategy, which improves the robustness and accuracy of the solution. The proposed algorithm was also tested under the CEC2013 test suite. Experimental results show that PTSSA is more competitive than some common algorithms. In addition, PTSSA is applied to optimize the operation of a heatless combined cooling-power system. Simulation results show that the optimized operation provided by PTSSA is more stable and efficient in terms of operating cost reduction.

Automated summary

This paper proposes an enhanced parallel salp swarm algorithm based on the Taguchi method (PTSSA), which improves the convergence rate and solution accuracy by splitting the initial population into subgroups and exchanging information among them. The Taguchi method is adopted as a communication strategy in the parallelization technique, enhancing the robustness and accuracy of the solution. Experimental results show that PTSSA is more competitive than common algorithms and it is also applied to optimize the operation of a combined cooling-power system, providing stable and efficient cost reduction.