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In view of various issues occasioned by harmonic pollution in the power system, the optimization of the filter parameters is of great significance. However, under the system constraints, estimating the filter parameters accurately and reliably is a challenging task. To complete this task, this paper proposes an improved salp swarm algorithm based on the spiral flight search strategy (ISSA-SFS), which rationally integrates the spiral flight search (SFS) strategy, the multiple leader (ML) strategy, and the random learning (RL) strategy with two improved evolution phases: the improved lead phase (ILP) and the improved follow phase (IFP). In the ILP, the SFS strategy is introduced to enhance the global search capability and avoid premature convergence. Furthermore, in the ILP, an ML strategy is proposed to select multiple leaders, further strengthening the global search capability of the proposed algorithm. In the IFP, a simple RL strategy is developed to learn two different random individuals, efficiently improving the local exploitation. The proposed algorithm ISSA-SFS is applied to optimize two prominent hybrid active power filter (HAPF) topologies, and each topology contains four different study cases. The overall experimental results indicate that the ISSA-SFS is a more promising alternative to achieve the optimal design of HAPFs compared with other well-established algorithms, especially in terms of accuracy and robustness.

An Improved Salp Swarm Algorithm With Spiral Flight Search for Optimizing Hybrid Active Power Filters’ Parameters