Integrated optimization algorithm: a metaheuristic approach for complicated optimization

This paper proposes an integrated optimization algorithm (IOA) designed for solving complicated optimization problems that are non-convex, non-differentiable, non-continuous, or computationally intensive. IOA is synthesized from 5 sub-algorithms: follower search, leader search, wanderer search, cros...

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Main Authors: Li, Chen, Chen, Guo, Liang, Gaoqi, Luo, Fengji, Zhao, Junhua, Dong, Zhao Yang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/163884
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1638842022-12-21T04:03:23Z Integrated optimization algorithm: a metaheuristic approach for complicated optimization Li, Chen Chen, Guo Liang, Gaoqi Luo, Fengji Zhao, Junhua Dong, Zhao Yang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Non-Convex Optimization Non-Differentiable Optimization This paper proposes an integrated optimization algorithm (IOA) designed for solving complicated optimization problems that are non-convex, non-differentiable, non-continuous, or computationally intensive. IOA is synthesized from 5 sub-algorithms: follower search, leader search, wanderer search, crossover search, and role learning. The follower search finds better solutions by tracing the leaders. The leader search refines current optimal solutions by approaching or deviating from the central point of the population and then executes a single-round coordinate descent. The wanderer search carries out comprehensive search space expansion. The crossover search generates offspring using solutions from superior parents. Role learning automates the process in which a search agent decides whether to become a follower or a wanderer. A global optima estimation framework (GOEF) is proposed to offer guidelines for designing an efficient optimization algorithm, and IOA is proved to attain global optima. A differentiable integrated optimization algorithm (DIOA) that extends gradient descent is put forward to train deep learning models. Empirical case studies conclude that IOA shows a much faster convergence speed and finds better solutions than the other 8 comparative algorithms based on 27 benchmark functions. IOA has also been applied to solve unit commitment problems in the power system and shows satisfactory results. A power line sub-image classification model based on a convolutional neural network (CNN) is optimized by DIOA. Compared with the pure gradient descent approach, DIOA converges significantly faster and obtains a high test set accuracy with much fewer training epochs. This work is partially supported by the Australian Research Council under Grants DP180103217, FT190100156, IH180100020, LP200100056 and partially supported by funding from the UNSW Digital Grid Futures Institute, UNSW, Sydney, under a cross-disciplinary fund scheme. 2022-12-21T04:03:22Z 2022-12-21T04:03:22Z 2022 Journal Article Li, C., Chen, G., Liang, G., Luo, F., Zhao, J. & Dong, Z. Y. (2022). Integrated optimization algorithm: a metaheuristic approach for complicated optimization. Information Sciences, 586, 424-449. https://dx.doi.org/10.1016/j.ins.2021.11.043 0020-0255 https://hdl.handle.net/10356/163884 10.1016/j.ins.2021.11.043 2-s2.0-85121259651 586 424 449 en Information Sciences © 2021 Elsevier Inc. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Non-Convex Optimization
Non-Differentiable Optimization
spellingShingle Engineering::Electrical and electronic engineering
Non-Convex Optimization
Non-Differentiable Optimization
Li, Chen
Chen, Guo
Liang, Gaoqi
Luo, Fengji
Zhao, Junhua
Dong, Zhao Yang
Integrated optimization algorithm: a metaheuristic approach for complicated optimization
description This paper proposes an integrated optimization algorithm (IOA) designed for solving complicated optimization problems that are non-convex, non-differentiable, non-continuous, or computationally intensive. IOA is synthesized from 5 sub-algorithms: follower search, leader search, wanderer search, crossover search, and role learning. The follower search finds better solutions by tracing the leaders. The leader search refines current optimal solutions by approaching or deviating from the central point of the population and then executes a single-round coordinate descent. The wanderer search carries out comprehensive search space expansion. The crossover search generates offspring using solutions from superior parents. Role learning automates the process in which a search agent decides whether to become a follower or a wanderer. A global optima estimation framework (GOEF) is proposed to offer guidelines for designing an efficient optimization algorithm, and IOA is proved to attain global optima. A differentiable integrated optimization algorithm (DIOA) that extends gradient descent is put forward to train deep learning models. Empirical case studies conclude that IOA shows a much faster convergence speed and finds better solutions than the other 8 comparative algorithms based on 27 benchmark functions. IOA has also been applied to solve unit commitment problems in the power system and shows satisfactory results. A power line sub-image classification model based on a convolutional neural network (CNN) is optimized by DIOA. Compared with the pure gradient descent approach, DIOA converges significantly faster and obtains a high test set accuracy with much fewer training epochs.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Chen
Chen, Guo
Liang, Gaoqi
Luo, Fengji
Zhao, Junhua
Dong, Zhao Yang
format Article
author Li, Chen
Chen, Guo
Liang, Gaoqi
Luo, Fengji
Zhao, Junhua
Dong, Zhao Yang
author_sort Li, Chen
title Integrated optimization algorithm: a metaheuristic approach for complicated optimization
title_short Integrated optimization algorithm: a metaheuristic approach for complicated optimization
title_full Integrated optimization algorithm: a metaheuristic approach for complicated optimization
title_fullStr Integrated optimization algorithm: a metaheuristic approach for complicated optimization
title_full_unstemmed Integrated optimization algorithm: a metaheuristic approach for complicated optimization
title_sort integrated optimization algorithm: a metaheuristic approach for complicated optimization
publishDate 2022
url https://hdl.handle.net/10356/163884
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