A mixed sine cosine butterfly optimization algorithm for global optimization and its application

Sushmita Sharma, Apu Kumar Saha, Susmita Roy, Seyedali Mirjalili, Sukanta Nama

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


This paper proposes a hybrid sine cosine butterfly optimization algorithm (m-SCBOA), in which a modified butterfly optimization algorithm is combined with sine cosine algorithm to achieve superior exploratory and exploitative search capabilities. The newly suggested m-SCBOA algorithm has been tested on 39 benchmark functions and compared with seven state-of-the-art algorithms. Moreover, it is tested with the CEC 2017 test suite, and the results are compared with seven more algorithms. Experimental results have demonstrated superior results of the proposed algorithm in nearly 75% of occasions on average whereas, similar results in nearly 20% of cases. Simulation results and convergence graphs show the supremacy of the m-SCBOA over the compared algorithms. The Friedman rank test validates that the m-SCBOA is statistically the best among the experiments. Additionally, four real-world engineering design problems and a priori multi-objective problem called parameter optimization of Al–4.5% Cu–TiC metal matrix composite are solved and the outcomes are compared to a wide range of algorithms. The results of these case studies establish the merits of m-SCBOA in solving challenging, real-world problems with unknown global optima.

Original languageEnglish
JournalCluster Computing
Publication statusPublished - 2022
Externally publishedYes


  • Analytic hierarchy process
  • Butterfly optimization algorithm
  • Optimization
  • Sine cosine algorithm


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