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Boosting salp swarm algorithm by opposition-based learning concept and sine cosine algorithm for engineering design problems

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Abstract

A unique hybrid meta-heuristic combining the salp swarm algorithm and the sine cosine algorithm (SSCA) is established in this study to improve convergence speed while outperforming existing conventional algorithms. The sine cosine position equations are utilized to update the position of the salp leader in search space while a weighting factor updates the position of the salp follower so that the best and possible optimal solutions are obtained using the sine or cosine and weighting function. Particle swarm optimization PSO inspires this weighting factor. Each salp uses the information-sharing approach of sine and cosine functions during this process to strengthen their exploration and exploitation abilities. The goal of incorporating modifications to the salp swarm optimizer algorithm is to help the standard approach avoid premature convergence that leads the search to the most likely search space. The proposed algorithm is tested on classical optimization benchmark functions and eight real engineering applications. The goal is to investigate and validate the SSCA's proper behaviour while finding the optimum solutions. The results of the comparison demonstrated that the SSCA method achieves the best accuracies.

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Authors and Affiliations

  1. Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421, Wroclaw, Poland

    Sumika Chauhan & Govind Vashishtha

  2. Precision Metrology Laboratory, Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, 148 106, India

    Govind Vashishtha

  3. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  4. School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia

    Laith Abualigah

  5. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon

    Laith Abualigah

  6. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  7. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  8. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  9. School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia

    Laith Abualigah

  10. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325 035, China

    Anil Kumar

Authors
  1. Sumika Chauhan
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  2. Govind Vashishtha
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  3. Laith Abualigah
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  4. Anil Kumar
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Contributions

SC: data curation, software, writing-original draft, methodology; GV: software, writing draft, methodology; LA: methodology, supervision; AK: writing-review & editing, supervision.

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Correspondence to Govind Vashishtha.

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Chauhan, S., Vashishtha, G., Abualigah, L. et al. Boosting salp swarm algorithm by opposition-based learning concept and sine cosine algorithm for engineering design problems. Soft Comput 27, 18775–18802 (2023). https://doi.org/10.1007/s00500-023-09147-z

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