Spiral-Inspired Spotted Hyena Optimizer and Its Application to Constraint Engineering Problems
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Spiral‑Inspired Spotted Hyena Optimizer and Its Application to Constraint Engineering Problems Vijay Kumar1 · Kamalinder Kaur Kaleka2 · Avneet Kaur2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper represents the modified version of spotted hyena optimizer (SHO) to improve the performance. The spiral moment and astrophysics concepts are utilized in the proposed algorithm. The spiral moment is used to enhance the intensification capability of SHO. The concept of astrophysics is incorporated in SHO to improve both diversification and intensification. The performance of the proposed algorithm is compared with five well-known metaheuristic algorithms over CEC 2015 benchmark test functions. The results depict that the proposed algorithm outperforms the others in terms of fitness function value. The effects of scalability and sensitivity analysis have also been investigated. The proposed algorithm is also applied to two constrained engineering design problems. The experimental result shows that the proposed algorithm performs better than the other algorithms. Keywords Optimization techniques · Metaheuristics · Swarm intelligence · Benchmark test functions · Engineering design problems
1 Introduction Now-a-days optimization is a very popular domain of research and is present in every field. The main purpose of the optimization is to attain maximum or minimum value of a function that is to either minimize or maximize a function. The earlier classical optimization approaches remain unsuccessful to present the adequate solutions for the real life problems. Hence the metaheuristics algorithms came into existence to solve these complex problems.
* Vijay Kumar [email protected] Kamalinder Kaur Kaleka [email protected] Avneet Kaur [email protected] 1
Computer Science and Engineering Department, National Institute of Technology, Hamirpur, Himachal Pradesh, India
2
Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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V. Kumar et al.
The metaheuristic algorithms [1] are classified into different categories which are evolutionary based, physical based and swarm based algorithms. Metaheuristic algorithms like Genetic Algorithm [2], Genetic Programming [3], Evolution Strategy [4] belongs to evolutionary based category. Simulated Annealing [5], Gravitational search algorithm [6], Big Bang Big Crunch [7], Small world optimization problem [8], Black Hole algorithm [9], Central Force Optimization [10], Artificial chemical reaction optimization algorithm [11], Ray optimization algorithm [12], Galaxy-based search algorithm [13] are some of the metaheuristic algorithms which comes under the physical based category. Some of the swarm based algorithms are Particle swarm optimization [14], Ant colony optimization [15], Dolphin partner optimization [16], Bat inspired optimization [17], Hunting search [18], Grey wolf optimization [19], Bee collecting pollen algorithm [20]. Exploration
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