A spring search algorithm applied to engineering optimization problems

Mohammad Dehghani, Zeinab Montazeri, Gaurav Dhiman, O. P. Malik, Ruben Morales-Menendez, Ricardo A. Ramirez-Mendoza*, Ali Dehghani, Josep M. Guerrero, Lizeth Parra-Arroyo

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

19 Citations (Scopus)
4 Downloads (Pure)


At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke's law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke's law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA's usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.

Original languageEnglish
Article number6173
JournalApplied Sciences (Switzerland)
Issue number18
Publication statusPublished - Sep 2020


  • Heuristic algorithms
  • Optimization
  • Spring
  • Spring force
  • Spring search


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