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

Area of Science:

• Engineering Optimization
• Computational Intelligence
• Algorithm Development

Background:

• Rao algorithms, introduced in 2020, are non-parametric optimization methods known for simplicity and effectiveness.
• Engineering optimization problems often demand robust and straightforward algorithms for optimal solutions.

Purpose of the Study:

• To develop a novel version of the Rao algorithm, named the Fully Informed Search Algorithm (FISA).
• To evaluate FISA's performance in optimizing real-world engineering problems while preserving the simplicity and non-parametric nature of existing Rao algorithms.

Main Methods:

• Development of the Fully Informed Search Algorithm (FISA) as a new variant of the Rao algorithms.
• Application of FISA to optimize benchmark functions (CEC 2005, CEC 2014) and design mechanical system components.
• Comparative analysis of FISA's results against the original RAO method.

Main Results:

• FISA demonstrates acceptable performance in optimizing complex, real-world engineering problems.
• The proposed FISA algorithm achieves optimized solutions comparable to the original RAO method.
• FISA maintains the simplicity and non-parametric characteristics of the foundational Rao algorithms.

Conclusions:

• The Fully Informed Search Algorithm (FISA) is an effective and efficient optimization tool for engineering applications.
• FISA presents a viable alternative for researchers seeking simple yet powerful optimization algorithms.
• MATLAB codes for FISA are available for public use, facilitating further research and application.