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A Disturbance-Estimation-Based Robust Optimization Control for Nonlinear Systems With Application to Wastewater Treatment Process

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第8期20卷 10428-10439页

作者： Zhang, Jiacheng Hou, Ying Han, Honggui Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China

The optimization control of nonlinear systems is crucial to improve system performance. However, uncertain disturbances of cost functions and system states make it difficult to solve optimal set points and realize stability control. For this problem, a disturbance-estimation-based robust optimization control (DERO-C) scheme is designed. First, a data-driven disturbance distribution evaluation strategy is proposed to detect the deviations in cost functions caused by disturbances. Then, the deviations are employed to approximate the distribution of uncertain disturbances. Second, an adaptive robust evolutionary algorithm is designed to solve the optimization problem. Then, the adaptive robustness indexes, derived from the disturbance distribution, are designed to guide the evolutionary process to improve the robustness of optimal set points. Third, a sliding mode controller with an adaptive reaching law is employed to track the optimal set points Finally, the optimal control performances of DERO-C are tested in the wastewater treatment process.

关键词： Data-driven strategy evolutionary computation robust optimization tracking control wastewater treatment process (WWTP)

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An Efficient Service-Aware Virtual Machine Scheduling Approach Based on Multi-Objective evolutionary Algorithm

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2027-2040页

作者： Xiao, Zhijiao Qiu, Qijie Li, Lingjie Feng, Yuhong Lin, Qiuzhen Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen 518118 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen Peoples R China

Service providers tend to deploy application services to several different virtual machines (VMs) to improve the scalability and manageability of the cloud data center (CDC). Therefore, high frequency communication traffic is always involved among those VMs that are deployed the same application service. In order to reduce the communication cost (CC) of CDC, all VMs running the same service should be redeployed in the same subnet as much as possible by using live migration technology, because CC between VMs in different subnets is much higher than that within the same subnet. On the other hand, the migration time (MT) to complete all migration tasks is also crucial for providers and customers, because a prolonged MT will lead to the increased maintenance cost and the deterioration of quality of service (QoS). To address the aforementioned issues, this paper proposes an efficient service-aware VM scheduling approach (ES-VSA) based on a multi-objective evolutionary algorithm to minimize CC and MT, simultaneously. Finally, experiments are conducted on four different scenarios, and the simulation results demonstrate that our proposed algorithm is superior to several state-of-the-art algorithms in terms of both CC and MT.

关键词： Costs Optimization Virtual machining evolutionary computation Cloud computing Heuristic algorithms Scalability multi-objective optimization evolutionary algorithm virtual machine live migration communication cost migration time

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Interactively Learning Rough Strategies That Dynamically Satisfy Investor's Preferences in Multiobjective Index Tracking

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IEEE TRANSACTIONS ON evolutionary computation 2024年第5期28卷 1412-1426页

作者： Soares Silva, Julio Cezar de Almeida Filho, Adiel Teixeira Univ Federalde Pernambuco Ctr Informat BR-50740560 Recife Brazil

Multiobjective index tracking models optimize portfolios considering investors' desire to replicate or outperform a market index. It is possible to search for the best portfolio in the optimal Pareto Front for a given investor with interactive multiobjective optimization using dominance-based rough sets approach (IMO-DRSA). However, obtaining the optimal Pareto front can be impractical as the index size grows. Therefore, evolutionary multiobjective approaches (EMO) can be used to find good fronts in a reasonable time. A simulated IMO-DRSA approach was adopted and extended to offer insights on how to design interactive processes for index tracking considering the stochastic output of EMO. The extended simulated IMO-DRSA approach analyzes how different factors, such as the number of interactions and methods for cognitive effort reduction, affect the capacity of an EMO to produce good portfolios for different types of artificial investors during interactions and to maintain their goodness over time. In contrast to previous studies, this research explores various approaches for displaying potential portfolios to investors and investigates the performance of an evolutionary algorithm guided by IMO-DRSA in multiple problem instances with an increased number of objectives.

关键词： Portfolios Indexes Optimization evolutionary computation Costs Process control Minimization Dominance-based rough set approach evolutionary algorithm index tracking interactive multiobjective optimization portfolio optimization

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SaDENAS: A self-adaptive differential evolution algorithm for neural architecture search

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SWARM AND evolutionary computation 2024年 91卷

作者： Han, Xiaolong Xue, Yu Wang, Zehong Zhang, Yong Muravev, Anton Gabbouj, Moncef Nanjing Univ Informat Sci & Technol Sch Software Nanjing Peoples R China Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN USA China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland

evolutionary neural architecture search (ENAS) and differentiable architecture search (DARTS) are all prominent algorithms in neural architecture search, enabling the automated design of deep neural networks. To leverage the strengths of both methods, there exists a framework called continuous ENAS, which alternates between using gradient descent to optimize the supernet and employing evolutionary algorithms to optimize the architectural encodings. However, in continuous ENAS, there exists a premature convergence issue accompanied by the small model trap, which is a common issue in NAS. To address this issue, this paper proposes a self-adaptive differential evolution algorithm for neural architecture search (SaDENAS), which can reduce the interference caused by small models to other individuals during the optimization process, thereby avoiding premature convergence. Specifically, SaDENAS treats architectures within the search space as architectural encodings, leveraging vector differences between encodings as the basis for evolutionary operators. To achieve a trade-off between exploration and exploitation, we integrate both local and global search strategies with a mutation scaling factor to adaptively balance these two strategies. Empirical findings demonstrate that our proposed algorithm achieves better performance with superior convergence compared to other algorithms.

关键词： Neural architecture search evolutionary computation Differential evolution Differentiable architecture search

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Multiplayer battle game-inspired optimizer for complex optimization problems

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第6期27卷 8307-8331页

作者： Xu, Yuefeng Zhong, Rui Zhang, Chao Yu, Jun Univ Fukui Sch Engn Fukui Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Univ Fukui Dept Engn Fukui Japan Niigata Univ Inst Sci & Technol Niigata Japan

Various popular multiplayer battle royale games share a lot of common elements. Drawing from our observations, we summarized these shared characteristics and subsequently proposed a novel heuristic algorithm named multiplayer battle game-inspired optimizer (MBGO). The proposed MBGO streamlines mainstream multiplayer battle royale games into two discrete phases: movement and battle. Specifically, the movement phase incorporates the principles of commonly encountered "safe zones" to incentivize participants to relocate to areas with a higher survival potential. The battle phase simulates a range of strategies players adopt in various situations to enhance the diversity of the population. To evaluate and analyze the performance of the proposed MBGO, we executed it alongside ten other algorithms, including three classics and five latest ones, across multiple diverse dimensions within the CEC2017 and CEC2020 benchmark functions. In addition, we employed several industrial design problems to evaluate the scalability and practicality of the proposed MBGO. The statistical analysis results reveal that the novel MBGO demonstrates significant competitiveness, excelling in convergence speed and achieving high levels of convergence accuracy across both benchmark functions and real-world problems.

关键词： evolutionary computation Meta-heuristic algorithm Multiplayer battle game-inspired optimizer Complex optimization

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Learning Difference Equations With Structured Grammatical Evolution for Postprandial Glycaemia Prediction

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第5期28卷 3067-3078页

作者： Parra, Daniel Joedicke, David Velasco, J. Manuel Kronberger, Gabriel Hidalgo, J. Ignacio Univ Complutense Madrid Dept Comp Architecture & Automatics Madrid 28040 Spain Univ Appl Sci Upper Austria A-4232 Hagenberg Austria

People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose management requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional methods, and also artificial neural networks, have shown high accuracy rates, sometimes they are not suitable for developing personalised treatments by physicians due to their lack of interpretability. This study proposes a novel glucose prediction method emphasising interpretability: Interpretable Sparse Identification by Grammatical Evolution. Combined with a previous clustering stage, our approach provides finite difference equations to predict postprandial glucose levels up to two hours after meals. We divide the dataset into four-hour segments and perform clustering based on blood glucose values for the two-hour window before the meal. Prediction models are trained for each cluster for the two-hour windows after meals, allowing predictions in 15-minute steps, yielding up to eight predictions at different time horizons. Prediction safety was evaluated based on Parkes Error Grid regions. Our technique produces safe predictions through explainable expressions, avoiding zones D (0.2% average) and E (0%) and reducing predictions on zone C (6.2%). In addition, our proposal has slightly better accuracy than other techniques, including sparse identification of non-linear dynamics and artificial neural networks. The results demonstrate that our proposal provides interpretable solutions without sacrificing prediction accuracy, offering a promising approach to glucose prediction in diabetes management that balances accuracy, interpretability, and computational efficiency.

关键词： Diabetes Insulin computational modeling machine learning system dynamics symbolic regression evolutionary computation neural networks

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Research on evolutionary Game of Pharmaceutical Supply Chain Platform Considering Government Supervision Behavior

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IEEE ACCESS 2024年 12卷 143930-143944页

作者： Wang, Yong Li, Yani Lu, Qian Zhang, Jiamin Zhang, Xiaoyu Ding, Ziruo Xu, Huiyi Wuhan Univ Sci & Technol Sch Management Wuhan 430080 Peoples R China KTH Royal Inst Technol Dept Prod Engn S-11428 Stockholm Sweden Wuhan Univ Sci & Technol Ctr Serv Sci & Engn Wuhan 430065 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China

Medicine occupies an important position in people's lives, and its safety and effectiveness are directly related to people's lives and health. In recent years, with the frequent occurrence of unqualified vaccines, quality control in drug transportation has been paid more and more attention. Therefore, this paper constructs a tripartite evolutionary game model composed of government, pharmaceutical enterprises, and logistics enterprises and models behavioral strategies. The interaction between the two and the influence of different parameters on the evolution are simulated and analyzed. The results show that implementing a government punishment mechanism, inter-firm revenue sharing and drug failure rate will have different impacts on the outcome of the evolutionary stability strategy of pharmaceutical enterprises and logistics enterprises. These conclusions not only enrich the theoretical literature on factors affecting the decision-making factors of pharmaceutical enterprises and logistics enterprises to ensure drug quality but also provide useful references for improving government policies.

关键词： Games Government policies Vaccines Costs Companies Green products Logistics Supply chain management Drug delivery Pharmaceuticals evolutionary computation Product safety Controlled products cold chain logistics evolutionary game pharmaceutical supply chain

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evolutionary Neural Architecture Search and Its Applications in Healthcare

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 143-185页

作者： Xin Liu Jie Li Jianwei Zhao Bin Cao Rongge Yan Zhihan Lyu School of Economics and Management Hebei University of TechnologyTianjin300401China School of Artificial Intelligence Hebei University of TechnologyTianjin300401China School of Electrical Engineering Hebei University of TechnologyTianjin300401China Department of Game Design Uppsala UniversityUppsalaS-75105Sweden

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error *** architecture search(NAS)attempts to detect effective architectures without human *** algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible *** multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered ***,hardware-accelerated NAS methods can improve the efficiency of the *** existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for *** this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS *** performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health *** for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare *** neural network has been successfully used in healthcare,but it lacks *** data is highly sensitive,and privacy leaks are frequently reported in the healthcare *** solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy ***,we also point out future research directions for evolutionary ***,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

关键词： Neural architecture search evolutionary computation large-scale multiobjective optimization distributed parallelism healthcare

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STEP: A Framework for Automated Point Cost Estimation

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IEEE TRANSACTIONS ON GAMES 2024年第4期16卷 927-936页

作者： Long, George E. M. Perez-Liebana, Diego Samothrakis, Spyridon Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

In miniature wargames, such as Warhammer 40k, players control asymmetrical armies, which include multiple units of different types and strengths. These games often use point costs to balance the armies. Each unit is assigned a point cost, and players have a budget they can spend on units. Calculating accurate point costs can be a tedious manual process, with iterative playtests required. If these point costs do not represent a units true power, the game can get unbalanced as overpowered units can have low point costs. In our previous paper, we proposed an automated way of estimating the point costs using a linear regression approach. We used a turn-based asymmetrical wargame called Wizard Wars to test our methods. Players were simulated using Monte Carlo tree search, using different heuristics to represent playstyles. We presented six variants of our method, and show that one method was able to reduce the unbalanced nature of the game by almost half. For this article, we introduce a framework called simple testing and evaluation of points, which allows for further and more granular analysis of point cost estimating methods, by providing a fast, simple, and configurable framework to test methods with. Finally, we compare how our methods do in Wizard Wars against expertly chosen point costs.

关键词： Games Costs Testing Manuals Linear regression evolutionary computation Artificial intelligence Automatic game balancing game testing wargames

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Testing the Robustness of JAYA Optimization on 3D Surface Alignment of Range Images: A Revised computational Study

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IEEE ACCESS 2024年 12卷 19009-19020页

作者： Santamaria, Jose Univ Jaen Dept Comp Sci Jaen 23071 Spain

The further development of image registration (IR) as automated image alignment techniques is a well-known concern in the field of computer vision (CV). These techniques have been implemented in many real-world scenarios: from remote sensing to medical imaging to artificial vision and computer-aided design. There is great interest in applying original optimization algorithms to overcome the challenges associated with early IR methods (e.g. the ICP algorithm). On the other hand, algorithms rooted in evolutionary theories in nature-inspired computational models such as evolutionary Computing (EC), have gained in importance over the past two decades. In addition, other algorithms with wide applicability fall into this category of methods, e.g. metaheuristics, swarmming, etc. Most of these methods have been widely adopted to address the IR problem, and are used as reliable alternatives for optimization goals. The aim of this paper is to address the following two main research challenges: 1) Bridging the gap in the revision of solutions proposed in recent years from the latter optimization model and from those ones from a new model based on deep learning (DL);and 2) introducing a new non-metaphor-based IR approach, called JAYA, to solve specific problems of aligning 3D surfaces of range images, also known as range image registration (RIR). In fact, as far as is known, this is the first time JAYA has been suggested for the above RIR problem. In particular, a new RIR method using the JAYA algorithm have been introduced and its performance has been accordingly compared against a wide set of methods from the SoTA. More than a dozen Softcomputing-based RIR methods have been included in the experimentation, making it the largest comparative study ever carried out in this category. In particular, range image datasets belonging to the SAMPL repository have been used, which has been widely adopted by many authors in the SoTA.

关键词： Computer vision evolutionary computation metaheuristics deep learning image registration JAYA

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