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24 results on '"Mittal, Sukrit"'

7. Learning to Converge Better: IP2 Operator

Author

Saxena, Dhish Kumar, Mittal, Sukrit, Deb, Kalyanmoy, Goodman, Erik D., Banzhaf, Wolfgang, Series Editor, Deb, Kalyanmoy, Series Editor, Saxena, Dhish Kumar, Mittal, Sukrit, and Goodman, Erik D.

Published
2024
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8. Optimization Problems and Algorithms

Author

Saxena, Dhish Kumar, Mittal, Sukrit, Deb, Kalyanmoy, Goodman, Erik D., Banzhaf, Wolfgang, Series Editor, Deb, Kalyanmoy, Series Editor, Saxena, Dhish Kumar, Mittal, Sukrit, and Goodman, Erik D.

Published
2024
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9. Conclusion and Future Research Directions

Author

Saxena, Dhish Kumar, Mittal, Sukrit, Deb, Kalyanmoy, Goodman, Erik D., Banzhaf, Wolfgang, Series Editor, Deb, Kalyanmoy, Series Editor, Saxena, Dhish Kumar, Mittal, Sukrit, and Goodman, Erik D.

Published
2024
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10. Introduction

Author

Saxena, Dhish Kumar, Mittal, Sukrit, Deb, Kalyanmoy, Goodman, Erik D., Banzhaf, Wolfgang, Series Editor, Deb, Kalyanmoy, Series Editor, Saxena, Dhish Kumar, Mittal, Sukrit, and Goodman, Erik D.

Published
2024
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11. Enhanced Innovized Repair Operator for Evolutionary Multi- and Many-objective Optimization

Author

Mittal, Sukrit, Saxena, Dhish Kumar, Deb, Kalyanmoy, and Goodman, Erik

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning, Computer Science - Performance
Abstract

"Innovization" is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems. Recent studies have shown that a chronological sequence of non-dominated solutions obtained in consecutive iterations during an optimization run also possess salient patterns that can be used to learn problem features to help create new and improved solutions. In this paper, we propose a machine-learning- (ML-) assisted modelling approach that learns the modifications in design variables needed to advance population members towards the Pareto-optimal set. We then propose to use the resulting ML model as an additional innovized repair (IR2) operator to be applied on offspring solutions created by the usual genetic operators, as a novel mean of improving their convergence properties. In this paper, the well-known random forest (RF) method is used as the ML model and is integrated with various evolutionary multi- and many-objective optimization algorithms, including NSGA-II, NSGA-III, and MOEA/D. On several test problems ranging from two to five objectives, we demonstrate improvement in convergence behaviour using the proposed IR2-RF operator. Since the operator does not demand any additional solution evaluations, instead using the history of gradual and progressive improvements in solutions over generations, the proposed ML-based optimization opens up a new direction of optimization algorithm development with advances in AI and ML approaches.

Published
2020

12. Embedding a Repair Operator in Evolutionary Single and Multi-objective Algorithms - An Exploitation-Exploration Perspective

Author

Deb, Kalyanmoy, Mittal, Sukrit, Saxena, Dhish Kumar, Goodman, Erik D., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ishibuchi, Hisao, editor, Zhang, Qingfu, editor, Cheng, Ran, editor, Li, Ke, editor, Li, Hui, editor, Wang, Handing, editor, and Zhou, Aimin, editor

Published
2021
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13. Innovative Design of Hydraulic Actuation System for Operator Fatigue Reduction and Its Optimization

Author

Mittal, Sukrit, Aggarwal, Divyam, Saxena, Dhish Kumar, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Salagame, Raviprakash R., editor, Ramu, Palaniappan, editor, Narayanaswamy, Indira, editor, and Saxena, Dhish Kumar, editor

Published
2020
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17. A Localized High-Fidelity-Dominance-Based Many-Objective Evolutionary Algorithm

Author

Saxena, Dhish Kumar, Mittal, Sukrit, Kapoor, Sarang, and Deb, Kalyanmoy

Abstract

The practicality of Pareto-dominance in solving many-objective optimization problems becomes questionable due to its inability to factor the critical human decision-making (HDM) elements, including the number of better objectives, the degree of betterment in objectives, and objectives’ relative preference. Relevant dominance principles are recently proposed to incorporate the first two HDM elements, often with the need for new tunable parameters. This article proposes a high-fidelity-dominance principle that factors all the three HDM elements, explicitly and simultaneously, and without requiring tuning of any parameter. This principle has been implemented in a reference-vector-based framework, leading to a computationally efficient many-objective evolutionary algorithm (MaOEA), namely, localized high-fidelity-dominance-based EA (LHFiD). Critically, LHFiD also has an inbuilt mechanism for on-the-fly determination of the timing for: 1) intermittent Nadir point estimation that enables faster convergence and 2) its self-termination that bears practically utility. This article is based on an extensive study involving 41 912 experiments, in which the proposed LHFiD approach is compared with the existing competitive MaOEAs. This article reports statistically better performance in about 60% instances, making it practical and worthy of further investigation and application.

Published
2023
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