A novel algorithm for global optimization: Rat Swarm Optimizer
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ORIGINAL RESEARCH
A novel algorithm for global optimization: Rat Swarm Optimizer Gaurav Dhiman1 · Meenakshi Garg1 · Atulya Nagar2 · Vijay Kumar3 · Mohammad Dehghani4 Received: 9 August 2019 / Accepted: 27 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Optimization · Metaheuristics · Swarm-intelligence · Benchmark test functions · Engineering design problems
1 Introduction For real world problems, stochastic optimization methods have been employed for solving various combinatorial problems. These optimization problems are non-linear, multimodal, computationally expensive, and possess large * Gaurav Dhiman [email protected] Meenakshi Garg [email protected] Atulya Nagar [email protected] Vijay Kumar [email protected] Mohammad Dehghani [email protected] 1
Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab 147001, India
2
Faculty of Science, Liverpool Hope University, Hope Park, Liverpool L16 9JD, UK
3
Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh 177001, India
4
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
solution spaces to solve traditional methods (Kaur et al. 2017, 2019, n.d., Kaur 2019; Kaur et al. 2019; Dhiman and Kumar 2017; Singh and Dhiman 2018a; Dhiman and Kumar 2018c; Singh and Dhiman 2018b). Metaheuristic algorithms are able to solve such complex problems (Che et al. 2019; Dhiman and Kumar 2018b; Dhiman and Kaur 2018; Singh et al. 2018a, b; Dhiman and Kumar 2018a; Kaur et al. 2018; Li et al. 2019; Asghari et al. 2020; Ramirez-Atencia and Camacho 2019) in a reasonable amount of time. Nowadays, there has been a lot of interest to develop metaheuristic optimization algorithms (Dhiman et al. 2018; Dhiman and Kumar 2019a, b; Dhiman and Kaur 2019b; Dhiman et al. 2019; Dhiman 2019a, b, c; Singh et al. 2019; Dehghani et al. 2019; Maini and Dhiman 2018; Pallavi and Dhi
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