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Symmetry
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Symmetry in Optimization and Its Applications to Machine Learning
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Symmetry in Optimization and Its Applications to Machine Learning

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 13663

Special Issue Editors

Prof. Dr. Hongfeng Wang

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: intelligent manufacturing, logistics and supply chain management; evolutionary computation; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rong Jiang

E-Mail Website
Guest Editor
Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming 650221, China
Interests: access control; privacy preserving; trustworthiness
Prof. Dr. Xujin Pu

E-Mail Website
Guest Editor
School of Business, Jiangnan University, Wuxi 214122, China
Interests: closed-loop supply chain; remanufacturing; reverse logistics

Special Issue Information

Dear Colleagues,

As a critical concept in understanding the laws of nature, symmetry has been well-investigated in the studies of mathematical optimizations. Over the past few decades, optimization has played a pivotal role in formulating and solving machine learning tasks, thus the connection between optimization and machine learning is becoming a popular research topic. There is no surprise that with the ever-increasing complexity of real-life tasks, both optimization and machine learning come with inherent facets of symmetry or asymmetry conveyed in different formal ways, which requires effective approaches to produce optimal solutions as well as efficient algorithms.

This special issue is focused on the methodologies and applications of coping with symmetry in optimization through the usage of concepts of machine learning. Research papers that employ theoretical analysis and/or practical applications in the related scopes are welcomed. Paper devoted to improving the interpretability and the computational efficiency of the symmetry constrained optimization models are also welcomed.

Prof. Dr. Hongfeng Wang
Prof. Dr. Rong Jiang
Prof. Dr. Xujin Pu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • iterative algorithm
  • heuristic method
  • efficiency
  • symmetry constrained optimization

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Published Papers (7 papers)

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Research

14 pages, 2658 KiB  
Article
An Interactive Estimation of the Distribution Algorithm Integrated with Surrogate-Assisted Fitness
by Zhanzhou Qiao, Guangsong Guo and Yong Zhang
Symmetry 2023, 15(10), 1852; https://doi.org/10.3390/sym15101852 - 2 Oct 2023
Viewed by 850
Abstract
To accurately model user preference information and ensure the symmetry or similarity between real user preference and the estimated value in product optimization design, an interactive estimation of a distribution algorithm integrated with surrogate-assisted fitness evaluation (SAF-IEDA) is proposed in this paper. Firstly, [...] Read more.
To accurately model user preference information and ensure the symmetry or similarity between real user preference and the estimated value in product optimization design, an interactive estimation of a distribution algorithm integrated with surrogate-assisted fitness evaluation (SAF-IEDA) is proposed in this paper. Firstly, taking the evaluation information of a few individuals as training data, a similarity evaluation method between decision variables is proposed. Following that, a preference probability model is built to estimate the distribution probability of decision variables. Then, the preference utility function of individuals is defined based on the similarity of decision variables. Finally, the surrogate-assisted fitness evaluation is realized by optimizing the weight of the decision variables’ similarities. The above strategies are incorporated into the interactive estimation of the distribution algorithm framework and applied to address the optimal product design problem and the indoor lighting optimization problem. The experimental results demonstrate that the proposed method outperforms the comparative method in terms of search efficiency and fitness prediction accuracy. Full article
(This article belongs to the Special Issue Symmetry in Optimization and Its Applications to Machine Learning)
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18 pages, 10495 KiB  
Article
Adaptive Reversible 3D Model Hiding Method Based on Convolutional Neural Network Prediction Error Expansion
by Guochang Hu, Kun Qian, Yinghua Li, Hong Li, Xinggui Xu and Hao Xu
Symmetry 2023, 15(9), 1782; https://doi.org/10.3390/sym15091782 - 18 Sep 2023
Viewed by 2134
Abstract
Although reversible data hiding technology is widely used, it still faces several challenges and issues. These include ensuring the security and reliability of embedded secret data, improving the embedding capacity, and maintaining the quality of media data. Additionally, irregular data types, such as [...] Read more.
Although reversible data hiding technology is widely used, it still faces several challenges and issues. These include ensuring the security and reliability of embedded secret data, improving the embedding capacity, and maintaining the quality of media data. Additionally, irregular data types, such as three-dimensional point clouds and triangle mesh-represented 3D models, lack an ordered structure in their representation. As a result, embedding these irregular data into digital media does not provide sufficient information for the complete recovery of the original data during extraction. To address this issue, this paper proposes a method based on convolutional neural network prediction error expansion to enhance the embedding capacity of carrier images while maintaining acceptable visual quality. The triangle mesh representation of the 3D model is regularized in a two-dimensional parameterization domain, and the regularized 3D model is reversibly embedded into the image. The process of embedding and extracting confidential information in carrier images is symmetrical, and the regularization and restoration of 3D models are also symmetrical. Experiments show that the proposed method increases the reversible embedding capacity, and the triangle mesh can be conveniently subjected to reversible hiding. Full article
(This article belongs to the Special Issue Symmetry in Optimization and Its Applications to Machine Learning)
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18 pages, 3206 KiB  
Article
Integrated Optimization of Blocking Flowshop Scheduling and Preventive Maintenance Using a Q-Learning-Based Aquila Optimizer
by Zhenpeng Ge and Hongfeng Wang
Symmetry 2023, 15(8), 1600; https://doi.org/10.3390/sym15081600 - 18 Aug 2023
Viewed by 1348
Abstract
In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability and efficiency of flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer (QL-AO) for solving the integrated optimization problem of blocking flowshop [...] Read more.
In recent years, integration of production scheduling and machine maintenance has gained increasing attention in order to improve the stability and efficiency of flowshop manufacturing systems. This paper proposes a Q-learning-based aquila optimizer (QL-AO) for solving the integrated optimization problem of blocking flowshop scheduling and preventive maintenance since blocking in the jobs processing requires to be considered in the practice manufacturing environments. In the proposed algorithmic framework, a Q-learning algorithm is designed to adaptively adjust the selection probabilities of four key population update strategies in the classic aquila optimizer. In addition, five local search methods are employed to refine the quality of the individuals according to their fitness level. A series of numerical experiments are carried out according to two groups of flowshop scheduling benchmark. Experimental results show that QL-AO significantly outperforms six peer algorithms and two state-of-the-art hybrid algorithms based on Q-Learning on the investigated integrated scheduling problem. Additionally, the proposed Q-learning and local search strategies are effective in improving its performance. Full article
(This article belongs to the Special Issue Symmetry in Optimization and Its Applications to Machine Learning)
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21 pages, 5333 KiB  
Article
Integrated Scheduling of Picking and Distribution of Fresh Agricultural Products for Community Supported Agriculture Mode
by Xujin Pu, Yuchen Xu and Yaping Fu
Symmetry 2022, 14(12), 2530; https://doi.org/10.3390/sym14122530 - 30 Nov 2022
Viewed by 1729
Abstract
Community Supported Agriculture (CSA), which offers two outstanding advantages, high-quality food and localized production, has come to the fore. In CSA, the output of picking scheduling is the input of delivery scheduling. Hence, only by scheduling the picking stage and distribution stage in [...] Read more.
Community Supported Agriculture (CSA), which offers two outstanding advantages, high-quality food and localized production, has come to the fore. In CSA, the output of picking scheduling is the input of delivery scheduling. Hence, only by scheduling the picking stage and distribution stage in a coordinated way can we achieve fresh agricultural products at minimum cost. However, due to asymmetric information in the picking and distribution stage, the integrated scheduling of picking and distribution may lead to an asymmetric optimization problem, which is suitable for solving with an iterative algorithm. Based on this, this work studies an integrated scheduling problem of the picking and distribution of fresh agricultural products with the consideration of minimizing picking and distribution costs as well as maximizing the freshness of orders. First, a nonlinear mixed-integer programming model for the problem under consideration is constructed. Second, a multi-objective multi-population genetic algorithm with local search (MOPGA-LS) is designed. Finally, the algorithm is compared with three multi-objective optimization algorithms in the literature: the non-dominated sorted genetic algorithm-II (NSGA-Ⅱ), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the multi-objective evolutionary algorithm based on decomposition that is combined with the bee algorithm (MOEA/D-BA). The comparison results show the excellent performance of the designed algorithm. Thus, the reported model and algorithm can assist managers and engineers in making well-informed decisions in managing the farm operation. Full article
(This article belongs to the Special Issue Symmetry in Optimization and Its Applications to Machine Learning)
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19 pages, 1697 KiB  
Article
Learning Path Optimization Based on Multi-Attribute Matching and Variable Length Continuous Representation
by Yong-Wei Zhang, Qin Xiao, Ying-Lei Song and Mi-Mi Chen
Symmetry 2022, 14(11), 2360; https://doi.org/10.3390/sym14112360 - 9 Nov 2022
Viewed by 1574
Abstract
Personalized learning path considers matching symmetrical attributes from both learner and learning material. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work [...] Read more.
Personalized learning path considers matching symmetrical attributes from both learner and learning material. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the matching of the following symmetrical attributes of learner/material: ability level/difficulty level, learning objective/covered concept, learning style/supported learning styles, and expected learning time/required learning time. The prerequisites of material are considered constraints. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show that the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency. Full article
(This article belongs to the Special Issue Symmetry in Optimization and Its Applications to Machine Learning)
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18 pages, 5322 KiB  
Article
The Way to Invest: Trading Strategies Based on ARIMA and Investor Personality
by Xiaoyu Tang, Sijia Xu and Hui Ye
Symmetry 2022, 14(11), 2292; https://doi.org/10.3390/sym14112292 - 1 Nov 2022
Cited by 3 | Viewed by 2525
Abstract
In the field of financial investment, accurate prediction of financial market values can increase investor profits. Investor personality affects specific portfolio solutions, which keeps them symmetrical in the process of investment competition. However, information is often asymmetric in financial markets, and this information [...] Read more.
In the field of financial investment, accurate prediction of financial market values can increase investor profits. Investor personality affects specific portfolio solutions, which keeps them symmetrical in the process of investment competition. However, information is often asymmetric in financial markets, and this information bias often results in different future returns for investors. Nowadays, machine learning algorithms are widely used in the field of financial investment. Many advanced machine learning algorithms can effectively predict future market changes and provide a scientific basis for investor decisions. The purpose of this paper is to study the problem of optimal matching of financial investment by using machine learning algorithms combined with finance and to reduce the impact of information asymmetry for investors effectively. Moreover, based on the model results, we study the effects of different investor personalities on factors such as expected investment returns and the number of transactions. Based on the time-series characteristics of price data, through multi-model comparison, we select the ARIMA model combined with particle swarm algorithm to determine the optimal prediction model and introduce the concepts of mean-variance model, Sharpe ratio, and efficient frontier to find the balance point of risk and return. In this study, we use gold and bitcoin price data from 2016–2021 to develop optimal investment strategies and study the impact of investor behavior on trading strategies. Full article
(This article belongs to the Special Issue Symmetry in Optimization and Its Applications to Machine Learning)
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19 pages, 3647 KiB  
Article
An Asymmetric Polling-Based Optimization Model in a Dynamic Order Picking System
by Dan Yang, Sen Liu and Zhe Zhang
Symmetry 2022, 14(11), 2283; https://doi.org/10.3390/sym14112283 - 31 Oct 2022
Cited by 2 | Viewed by 1313
Abstract
The timeliness of order deliveries seriously impacts customers’ evaluation of logistics services and, hence, has increasingly received attention. Due to the diverse and large quantities of orders under the background of electronic commerce, how to pick orders efficiently while also adapting these features [...] Read more.
The timeliness of order deliveries seriously impacts customers’ evaluation of logistics services and, hence, has increasingly received attention. Due to the diverse and large quantities of orders under the background of electronic commerce, how to pick orders efficiently while also adapting these features has become one of the most challenging problems for distribution centers. However, previous studies have not accounted for the differences in the stochastic characteristics of order generation, which may lead to asymmetric optimization problems. With this in mind, a new asymmetric polling-based model that assumes the varying stochastic characteristics to analyze such order picking systems is put forward. In addition, two important indicators of the system, mean queue length (MQL) and mean waiting time (MWT), are derived by using probability-generating functions and the embedded Markov chain. Then, simulation experiments and a comparison of the numerical and theoretical results are conducted, showing the usefulness and practicalities of the proposed model. Finally, the paper discusses the characteristics of the novel order picking system and the influence of the MQL and MWT on it. Full article
(This article belongs to the Special Issue Symmetry in Optimization and Its Applications to Machine Learning)
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