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A novel algorithm for global optimization: Rat Swarm Optimizer

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Abstract

This paper presents a novel bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO) for solving the challenging optimization problems. The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. This paper mathematically models these behaviors and benchmarks on a set of 38 test problems to ensure its applicability on different regions of search space. The RSO algorithm is compared with eight well-known optimization algorithms to validate its performance. It is then employed on six real-life constrained engineering design problems. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of proposed algorithm. The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms. Note that the source codes of the proposed technique are available at: http://www.dhimangaurav.com.

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Acknowledgements

The first and corresponding author Dr. Gaurav Dhiman would like to thanks to his father Mr. Rajinder Dhiman and mother Mrs. Kamlesh Dhiman for their support and divine blessings on him.

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Authors and Affiliations

  1. Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab, 147001, India

    Gaurav Dhiman & Meenakshi Garg

  2. Faculty of Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, UK

    Atulya Nagar

  3. Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, 177001, India

    Vijay Kumar

  4. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Mohammad Dehghani

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  1. Gaurav Dhiman
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Dhiman, G., Garg, M., Nagar, A. et al. A novel algorithm for global optimization: Rat Swarm Optimizer. J Ambient Intell Human Comput 12, 8457–8482 (2021). https://doi.org/10.1007/s12652-020-02580-0

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