A new metaphor-less simple algorithm based on Rao algorithms: a Fully Informed Search Algorithm (FISA)

Mojtaba Ghasemi; Abolfazl Rahimnejad; Ebrahim Akbari; Ravipudi Venkata Rao; Pavel Trojovský; Eva Trojovská; Stephen Andrew Gadsden

doi:10.7717/peerj-cs.1431

A new metaphor-less simple algorithm based on Rao algorithms: a Fully Informed Search Algorithm (FISA)

PeerJ Comput Sci. 2023 Aug 4:9:e1431. doi: 10.7717/peerj-cs.1431. eCollection 2023.

Authors

Mojtaba Ghasemi^#¹, Abolfazl Rahimnejad^#², Ebrahim Akbari³, Ravipudi Venkata Rao⁴, Pavel Trojovský³, Eva Trojovská³, Stephen Andrew Gadsden²

Affiliations

¹ Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.
² Department of Mechanical Engineering, McMaster University, Hamilton, Canada.
³ Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Králové, Czech Republic.
⁴ Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Ichchanath, Surat, Gujarat, India.

^# Contributed equally.

Abstract

Many important engineering optimization problems require a strong and simple optimization algorithm to achieve the best solutions. In 2020, Rao introduced three non-parametric algorithms, known as Rao algorithms, which have garnered significant attention from researchers worldwide due to their simplicity and effectiveness in solving optimization problems. In our simulation studies, we have developed a new version of the Rao algorithm called the Fully Informed Search Algorithm (FISA), which demonstrates acceptable performance in optimizing real-world problems while maintaining the simplicity and non-parametric nature of the original algorithms. We evaluate the effectiveness of the suggested FISA approach by applying it to optimize the shifted benchmark functions, such as those provided in CEC 2005 and CEC 2014, and by using it to design mechanical system components. We compare the results of FISA to those obtained using the original RAO method. The outcomes obtained indicate the efficacy of the proposed new algorithm, FISA, in achieving optimized solutions for the aforementioned problems. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/FISA.

Keywords: Constrained engineering optimization; Fully Informed Search Algorithm (FISA); Optimization; Rao algorithms.

Grants and funding

The research was supported by the Project of Excellence of Faculty of Science, University of Hradec Kralove, Czech Republic, No. 2210/2023-2024. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.