Hybridizing salp swarm algorithm with particle swarm optimization algorithm for recent optimization functions
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RESEARCH PAPER
Hybridizing salp swarm algorithm with particle swarm optimization algorithm for recent optimization functions Narinder Singh1 · S. B. Singh1 · Essam H. Houssein2 Received: 19 January 2020 / Revised: 29 June 2020 / Accepted: 9 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The salp swarm algorithm (SSA) has shown its fast search speed in several challenging problems. Research shows that not every nature-inspired approach is suitable for all applications and functions. Additionally, it does not provide the best exploration and exploitation for each function during the search process. Therefore, there were several researches attempts to improve the exploration and exploitation of the meta-heuristics by developing the newly hybrid approaches. This inspired our current research and therefore, we developed a newly hybrid approach called hybrid salp swarm algorithm with particle swarm optimization for searching the superior quality of optimal solutions of the standard and engineering functions. The hybrid variant integrates the advantages of SSA and PSO to eliminate many disadvantages such as the trapping in local optima and the unbalanced exploitation. We have used the velocity phase of the PSO approach in salp swarm approach in order to avoid the premature convergence of the optimal solutions in the search space, escape from ignoring in local minima and improve the exploitation tendencies. The new approach has been verified on different dimensions of the given functions. Additionally, the proposed technique has been compared with a wide range of algorithms in order to confirm its efficiency in solving standard CEC 2005, CEC 2017 test suits and engineering problems. The simulation results show that the proposed hybrid approach provides competitive, often superior results as compared to other existing algorithms in the research community. Keywords Standard functions · Heuristic hybridization · Salp swarm algorithm · Particle swarm optimization algorithm · Exploration and exploitation
1 Introduction The last few decades witnessed the introduction of many robust population-based meta-heuristics, such as swarm intelligence optimization and evolutionary algorithms for the purpose of finding the best and possible optimal solutions to many real-life applications. Although such algorithms have been useful in tackling many real life problems, the extensive literature review reveals that there is no single * Narinder Singh [email protected] S. B. Singh [email protected] Essam H. Houssein [email protected] 1
Department of Mathematics, Punjabi University, Patiala, India
Faculty of Computers and Information, Minia University, El‑Minia Governorate, Egypt
2
algorithm which works well in all the applications. Consequently, researchers have been developing newly, modified and hybrid techniques for resolving and eliminate many of the disadvantages of the existing algorithms such as: differential evolution (DE) [1, 2], genetic algorithm (GA) [3, 4], hybrid gen
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