A multi-strategy spider wasp optimizer based on grouping and dimensional symmetry method with a time-varying weight

Metaheuristic algorithms offer numerous advantages, such as their ability to address a wide range of problems without the need for specific problem formulations or gradient information. They have demonstrated effectiveness in solving complex real-world problems. The Spider Wasp Optimizer (SWO) is an innovative metaheuristic algorithm inspired by the hunting, nesting, and mating behaviors of female spider wasps. In order to improve its overall efficiency and enhance the exploitation performance of SWO, this paper introduces three strategies for the enhancement of SWO. The modified algorithm resulting from these enhancements is referred to as ADNSWO. Through the application of the Adaptive Group-Based Strategy Application (AGSA), individuals within the population not involved in exploitation are grouped, and the population’s exploitation strategy is enhanced using the Dimensional Symmetric Distance Optimization Strategy (DSD). AGSA and DSD strategies enhance the exploitation performance of the population. The third strategy aims to enhance the equilibrium between exploration and exploitation in the population by employing the Time-varying Weight (TW). To evaluate ADNSWO’s performance, numerical optimization experiments were conducted using the CEC2017 test set. ADNSWO was compared with SWO and eight other state-of-the-art algorithms. Statistical analysis, including the Wilcoxon Signed-Rank Test and the Friedman test, revealed that ADNSWO outperformed other algorithms comprehensively. Furthermore, an analysis of the computational complexity of ADNSWO showed that its performance improvement did not come at the cost of increased complexity. The effectiveness analysis of the strategies demonstrated that the three proposed strategies significantly contributed to the overall performance enhancement of ADNSWO.