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Resistance-capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial

Sowmya Ravichandran¹, Premkumar Manoharan², Pradeep Jangir³

¹Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Scientific Reports

|September 23, 2023

Summary

This summary is machine-generated.

A new physics-inspired algorithm, the Resistance Capacitance Optimization Algorithm (RCOA), shows strong effectiveness and reliability for complex engineering design optimization problems. It outperforms existing methods without needing tunable parameters.

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Area of Science:

* Computational Intelligence
* Optimization Algorithms
* Engineering Design

Background:

* Metaheuristic algorithms are crucial for solving complex optimization problems.
* Existing algorithms often require parameter tuning, impacting their generalizability.
* Physics-inspired approaches offer novel solutions to optimization challenges.

Purpose of the Study:

* To introduce and validate the Resistance Capacitance Optimization Algorithm (RCOA).
* To assess RCOA's performance on benchmark functions and engineering design problems.
* To evaluate RCOA's effectiveness, reliability, and precision in optimization tasks.

Main Methods:

* Applied the Resistance Capacitance Optimization Algorithm (RCOA) to 23 benchmark test functions.
* Evaluated RCOA on eight constrained engineering design optimization problems.
* Conducted rigorous statistical non-parametric testing to validate performance.

Main Results:

* RCOA demonstrated significant effectiveness compared to state-of-the-art algorithms.
* The algorithm performed reliably and precisely on complex engineering design challenges.
* RCOA operates effectively without any control or tunable parameters.

Conclusions:

* RCOA is a highly effective and reliable tool for constrained engineering design optimization.
* The physics-inspired RCOA shows potential as a transformative tool in mathematical optimization.
* Further research into physics-inspired algorithms for optimization is warranted.