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Article History

Accepted: 22 April 2021

First Online: 19 May 2021

Declarations

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: Dr. Narinder Singh and Mrs. Jaspreet Kaur declare that they have no conflict of interest.

: Harmony search approach was firstly developed by Z.W. Geem et al. Geem et al. (), Geem (). The combination search is a music-inspired optimization technique. It is inspired by the criticism that the aim of music is to search for a perfect state of harmony.On the other side, Seyedali Mirjalili Mirjalili () developed a new nature-inspired approach known as sine–cosine algorithm (SCA) for solving different types of application of separate fields. This approach establishes the solution of various basic random agents and enables them to exclude them from a mathematical model based on the trigonometric sine and cosine functions as the best possible outcomes. I have studied theoretical models developed by various researchers, viz. genetic algorithm (GA) Chung and Li (), Cai et al. (), particle swarm optimization (PSO) Kennedy and Eberhart (), ant colony optimization (ACO) Soares et al. (), differential evolution (DE) Kumar and Chandrasekar (), Kumar and Chandrasekar (), hybrid genetic algorithm (HGA) Slimani and Bouktir (), fuzzy-based hybrid particle swarm optimization (fuzzy HPSO) Hsun et al. (), harmony search algorithm Sinsupan et al. (), robust optimization (RO) Ben-Tal et al. (), artificial neural network (ANN) Chowdhury (), biogeography-based optimization algorithm (BBO) Simon (), gray wolf optimization (GWO) Mirjalili et al. (), tabu search (TS) Abido (), krill herd algorithm (KHA) Mukherjee and Mukherjee (), ant lion optimizer (ALO) Mirjalili (), gravitational search algorithm (GSA) Duman et al. (), sine–cosine algorithm (SCA) Mirjalili (), dragonfly algorithm (DA) Mirjalili (), black hole-based optimization (BHBO) Bouchekara (), whale optimization algorithm (WOA) Mirjalili (), adaptive group search optimization (AGSO) Daryani et al. (), multi-verse optimizer (MVO) Daryani et al. (), moth flame optimizer (MFO) Mirjalili (), cuckoo search (CS) Mirjalili (), grasshopper optimization algorithm (GOA) Mirjalili (), one half personal best position particle swarm optimization (OHGBPPSO) Singh and Singh (), personal best position particle swarm optimization (PBPPSO) Singh and Singh (), half mean particle swarm optimization algorithm (HMPSO) Singh et al. (), HAGWO Singh and Hachimi (), hybrid particle swarm optimization (HPSO) Singh et al. (), HPSOGWO Singh and Singh (), hybrid MGBPSO-GSA Singh and Singh (), HGWOSCA Singh and Singh (), MGWO Singh and Singh (), MVGWO Singh (), HSSAPSO Singh et al. (), SChoA Kaur et al. (), HSSASCA Singh et al. () and many more. Based on the work done by these authors, we have also proposed an hybrid approach, namely “hybrid sine–cosine algorithm–harmony search algorithm (HSCAHS)”. With this method, it is proposed to increase the convergence quality of the sine–cosine algorithm by accelerating the explore seeking instead of letting the approach running numerous iterations without any perfection. The new hybrid approach has been tested with numerous well-known standard test functions and some real-life applications. All experimental solutions ensured that the newer current access is a strong search approach for different compatibility applications. This article does not contain any studies with human participants or animals performed by any of the authors.

: No human/animal was involved in the current study. So informed consent is not applicable on my study.