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Spider wasp optimizer: a novel meta-heuristic optimization algorithm

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Abstract

This work presents a new nature-inspired meta-heuristic algorithm named spider wasp optimization (SWO) 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. Specifically, SWO achieves an overall effective percentage of 78.2% on the standard benchmark, 92.31% on CEC2014, 77.78% on CEC2017, 60% on CEC2020, and 100% on real-world problems. The source code of SWO is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/126010-spider-wasp-optimizer-swo.

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Authors and Affiliations

  1. Faculty of Computers and Informatics, Zagazig University, Shaibet an Nakareyah, Zagazig, 44519, Ash Sharqia Governorate, Egypt

    Mohamed Abdel-Basset & Reda Mohamed

  2. Department of Mathematics, Sana’a University, 13509, Sana’a, Yemen

    Mohammed Jameel

  3. Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt

    Mohamed Abouhawwash

  4. Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA

    Mohamed Abouhawwash

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  1. Mohamed Abdel-Basset
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  2. Reda Mohamed
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  3. Mohammed Jameel
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  4. Mohamed Abouhawwash
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Correspondence to Mohamed Abouhawwash.

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Appendix A

Appendix A

See Table 18.

See Fig. 24.

See Tables 19, 20, 21.

Table 18 Description of some standard test functions
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Fig. 24
figure 24

Topologies of some mathematical test functions in standard benchmark

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Table 19 Properties of 18 CEC-2017 test functions
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Table 20 Properties of 13 CEC-2014 test functions
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Table 21 Characteristics of CEC-2020 test functions
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Abdel-Basset, M., Mohamed, R., Jameel, M. et al. Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artif Intell Rev 56, 11675–11738 (2023). https://doi.org/10.1007/s10462-023-10446-y

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