Evolutionary Multi-objective Optimization - Methods and Metrics

Authors

  • Luca Rossi Professor of AI and Healthcare Systems, Roma University, Rome, Italy Author

Keywords:

Evolutionary Multi-objective Optimization, EMO, Multi-objective Optimization, Genetic Algorithms

Abstract

Evolutionary Multi-objective Optimization (EMO) is a powerful technique for solving complex optimization problems with multiple conflicting objectives. This paper provides a comprehensive review of methods and metrics used in EMO, focusing on their principles, advantages, and applications. The paper begins by introducing the concept of multi-objective optimization and the challenges it poses. It then explores various EMO algorithms, including genetic algorithms, particle swarm optimization, and differential evolution, highlighting their strengths and weaknesses. Additionally, the paper discusses the importance of performance metrics in evaluating EMO algorithms, such as hypervolume, inverted generational distance, and epsilon indicator. The insights provided in this paper aim to enhance understanding and promote further research in the field of EMO.

Downloads

Download data is not yet available.

References

Venigandla, Kamala, and Venkata Manoj Tatikonda. "Improving Diagnostic Imaging Analysis with RPA and Deep Learning Technologies." Power System Technology 45.4 (2021).

Vemuri, Navya, and Kamala Venigandla. "Autonomous DevOps: Integrating RPA, AI, and ML for Self-Optimizing Development Pipelines." Asian Journal of Multidisciplinary Research & Review 3.2 (2022): 214-231.

Palle, Ranadeep Reddy. "The convergence and future scope of these three technologies (cloud computing, AI, and blockchain) in driving transformations and innovations within the FinTech industry." Journal of Artificial Intelligence and Machine Learning in Management 6.2 (2022): 43-50.

Raparthi, Mohan, et al. "Advancements in Natural Language Processing-A Comprehensive Review of AI Techniques." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 1-10.

Reddy, Surendranadha Reddy Byrapu. "Enhancing Customer Experience through AI-Powered Marketing Automation: Strategies and Best Practices for Industry 4.0." Journal of Artificial Intelligence Research 2.1 (2022): 36-46.

Downloads

Published

04-12-2022

How to Cite

[1]
Luca Rossi, “Evolutionary Multi-objective Optimization - Methods and Metrics”, J. of Artificial Int. Research and App., vol. 2, no. 2, pp. 1–9, Dec. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/12

Similar Articles

1-10 of 125

You may also start an advanced similarity search for this article.