Pareto Multi Objective Optimization
Abstract
The goal of this chapter is to give fundamental knowledge on solving multi-objective optimization problems. The focus is on the intelligent metaheuristic approaches (evolutionary algorithms or swarm-based techniques). The focus is on techniques for efficient generation of the Pareto frontier. A general formulation of MO optimization is given in this chapter, the Pareto optimality concepts introduced, and solution approaches with examples of MO problems in the power systems field are given
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New Bioinspired Algorithms (BIAs) have been developed to help overcome the limitations of traditional AI algorithms, especially for optimization of multiobjective problems.
“In recent years, new Bioinspired Algorithms (BIAs) have been developed to help overcome the limitations of traditional AI algorithms, especially when it concerns the optimization of multiobjective problems [53].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [53]
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Sorted by citations
Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00026 · Cited by 200 · see citing works
Xu, W., Diesen, E., He, T., Reuter, K., & Margraf, J. T. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.3c14486 · Cited by 151 · see citing works
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“In recent years, new Bioinspired Algorithms (BIAs) have been developed to help overcome the limitations of traditional AI algorithms, especially when it concerns the optimization of multiobjective problems [53].”
Background
- Authors
- Ngatchou, P., Zarei, A., & El-Sharkawi, A.
- Affiliations
- University of Washington (US)
- Venue
- Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems
- Publisher
- IEEE
- Published
- 2006-03-21
- Pages
- 84-91
- OpenAlex
- W2535351973
- Cited by
- 664
- References
- 0
- Citations by year
- 2026: 26 · 2025: 78 · 2024: 84 · 2023: 70 · 2022: 53 · 2021: 46 · 2020: 39 · 2019: 43 · 2018: 34 · 2017: 24 · 2016: 20 · 2015: 19 · 2014: 33 · 2013: 16 · 2012: 17
- FWCI
- 4.35
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent System Applications to Power Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- multi-objective optimization, Pareto optimality, Pareto front, evolutionary algorithms, swarm intelligence, metaheuristic optimization
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