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Criminal Search Optimization Algorithm: A Population-Based Meta-Heuristic Optimization Technique to Solve Real-World Optimization Problems

  • Research Article-Electrical Engineering
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Abstract

Optimization techniques are widely used to solve variety of problems related to the fields of engineering, statistics, finance, etc. In this article, a new optimization algorithm named criminal search optimization algorithm (CSOA) has been proposed. This proposed algorithm is inspired by policemen and replicates the strategies and intelligence used by a team of the policemen to catch a criminal for a crime. The performance of CSOA is validated using two suites of benchmark functions (CEC-2005 and CEC-2020). Further, the proposed method is used to solve a multi-objective real-world optimization problem, i.e. a combined emission economic dispatch problem. To evaluate the performance of the proposed method, five test cases have been considered in this study. The results obtained are compared with other existing well-known optimization methods to show the superiority of the proposed CSOA method.

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Notes

  1. As, the information of the suspect with the SI and Informer constitutes the problem variables D; therefore, during the final computation \(SI_{\text {max}}\) and \(Inf_{\text {max}}\) are considered as \(SI_{\text {max}} \times D\) and \(Inf_{\text {max}} \times D\). Therefore, for worst case scenario \(n^2\) is considered.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, India

    Abhishek Srivastava & Dushmanta Kumar Das

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  1. Abhishek Srivastava
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  2. Dushmanta Kumar Das
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Srivastava, A., Das, D.K. Criminal Search Optimization Algorithm: A Population-Based Meta-Heuristic Optimization Technique to Solve Real-World Optimization Problems. Arab J Sci Eng 47, 3551–3571 (2022). https://doi.org/10.1007/s13369-021-06446-1

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