Spider Wasp Optimizer (SWO)

Spider Wasp Optimizer (SWO) is a nature-inspired meta-heuristic algorithm

Spider Wasp Optimizer (SWO) is a nature-inspired meta-heuristic algorithm, which is based on replicating the hunting, nesting, and mating behaviors of the female spider wasps in nature. This proposed algorithm has various unique updating strategies, making it applicable to a wide range of optimization problems with different exploration and exploitation requirements. The proposed SWO is compared with nine newly published and well-established metaheuristics over four different benchmarks: (1) Standard benchmark, including 23 unimodal and multimodal test functions; (2) test suite of CEC2017, (3) test suite of CEC2020, and (4) test suite of CEC2014 to validate its reliability. Moreover, two classical engineering design problems, namely, welded bean and pressure vessel designs, and parameter estimation of the single-diode, double-diode, and triple-diode photovoltaic models are used to further evaluate the performance of SWO in optimizing real-world optimization problems. Experimental findings demonstrate that SWO is more competitive compared with the state-of-art meta-heuristic methods for four validated benchmarks and superior to all observed real-world optimization problems.

Main paper: Abdel-Basset, M., Mohamed, R., Jameel, M., & Abouhawwash, M. (2023). Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artificial Intelligence Review.