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On Adaptive Learning Framework for Deep Weighted Sparse Autoencoder: A Multiobjective evolutionary Algorithm

IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3221-3231页

作者： Cheng, Hanjing Wang, Zidong Wei, Zhihui Ma, Lifeng Liu, Xiaohui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

In this article, an adaptive learning framework is established for a deep weighted sparse autoencoder (AE) by resorting to the multiobjective evolutionary algorithm (MOEA). The weighted sparsity is introduced to facilitate the design of the varying degrees of the sparsity constraints imposed on the hidden units of the AE. The MOEA is exploited to adaptively seek appropriate hyperparameters, where the divide-and-conquer strategy is implemented to enhance the MOEA's performance in the context of deep neural networks. Moreover, a sharing scheme is proposed to further reduce the time complexity of the learning process at the slight expense of the learning precision. It is shown via extensive experiments that the established adaptive learning framework is effective, where different sparse models are utilized to demonstrate the generality of the proposed results. Then, the generality of the proposed framework is examined on the convolutional AE and VGG-16 network. Finally, the developed framework is applied to the blind image quantity assessment that illustrates the applicability of the established algorithms.

关键词： Optimization Training Adaptive learning evolutionary computation Artificial neural networks Decoding Adaptive learning framework deep neural network (DNN) evolutionary algorithm multiobjective optimization algorithm weighted sparseness

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An Ensemble Surrogate-Based Framework for Expensive Multiobjective evolutionary Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2022年第4期26卷 631-645页

作者： Lin, Qiuzhen Wu, Xunfeng Ma, Lijia Li, Jianqiang Gong, Maoguo Coello, Carlos A. Coello Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China CINVESTAV IPN Comp Sci Dept Mexico City 07360 DF Mexico Basque Ctr Appl Math Bilbao 48009 Spain Ikerbasque Bilbao 48009 Spain

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases.

关键词： Optimization computational modeling Predictive models evolutionary computation Uncertainty Training Space exploration Ensemble surrogate evolutionary algorithms model management multiobjective optimization

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An Adaptive Reference Vector-Guided evolutionary Algorithm Using Growing Neural Gas for Many-Objective Optimization of Irregular Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 2698-2711页

作者： Liu, Qiqi Jin, Yaochu Heiderich, Martin Rodemann, Tobias Yu, Guo Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Honda R&D Europe Deutschland GmbH FTR Dept D-63073 Offenbach Germany Honda Res Inst Europe FTR Dept D-63073 Offenbach Germany

Most reference vector-based decomposition algorithms for solving multiobjective optimization problems may not be well suited for solving problems with irregular Pareto fronts (PFs) because the distribution of predefined reference vectors may not match well with the distribution of the Pareto-optimal solutions. Thus, the adaptation of the reference vectors is an intuitive way for decomposition-based algorithms to deal with irregular PFs. However, most existing methods frequently change the reference vectors based on the activeness of the reference vectors within specific generations, slowing down the convergence of the search process. To address this issue, we propose a new method to learn the distribution of the reference vectors using the growing neural gas (GNG) network to achieve automatic yet stable adaptation. To this end, an improved GNG is designed for learning the topology of the PFs with the solutions generated during a period of the search process as the training data. We use the individuals in the current population as well as those in previous generations to train the GNG to strike a balance between exploration and exploitation. Comparative studies conducted on popular benchmark problems and a real-world hybrid vehicle controller design problem with complex and irregular PFs show that the proposed method is very competitive.

关键词： Optimization Sociology Statistics evolutionary computation Convergence Clustering algorithms Topology evolutionary many-objective optimization growing neural gas (GNG) irregular Pareto front (PF) reference vector

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Clustering Ensemble Based on Hybrid Multiview Clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 6518-6530页

作者： Yu, Zhiwen Wang, Daxing Meng, Xian-Bing Chen, C. L. Philip Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Macau Fac Sci & Technol Macau 99999 Peoples R China

As an effective method for clustering applications, the clustering ensemble algorithm integrates different clustering solutions into a final one, thus improving the clustering efficiency. The key to designing the clustering ensemble algorithm is to improve the diversities of base learners and optimize the ensemble strategies. To address these problems, we propose a clustering ensemble framework that consists of three parts. First, three view transformation methods, including random principal component analysis, random nearest neighbor, and modified fuzzy extension model, are used as base learners to learn different clustering views. A random transformation and hybrid multiview learning-based clustering ensemble method (RTHMC) is then designed to synthesize the multiview clustering results. Second, a new random subspace transformation is integrated into RTHMC to enhance its performance. Finally, a view-based self-evolutionary strategy is developed to further improve the proposed method by optimizing random subspace sets. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method for clustering different kinds of data.

关键词： Clustering algorithms Kernel Machine learning algorithms Research and development Machine learning Fuses evolutionary computation Cluster ensemble ensemble learning multiview clustering random subspace transformation

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A Reference Vector-Based Simplified Covariance Matrix Adaptation Evolution Strategy for Constrained Global Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3696-3709页

作者： Kumar, Abhishek Das, Swagatam Mallipeddi, Rammohan Indian Inst Technol Varanasi Dept Elect Engn Varanasi 221005 Uttar Pradesh India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India Kyungpook Natl Univ Coll IT Engn Dept Artificial Intelligence Daegu 41566 South Korea

During the last two decades, the notion of multiobjective optimization (MOO) has been successfully adopted to solve the nonconvex constrained optimization problems (COPs) in their most general forms. However, such works mainly utilized the Pareto dominance-based MOO framework while the other successful MOO frameworks, such as the reference vector (RV) and the decomposition-based ones, have not drawn sufficient attention from the COP researchers. In this article, we utilize the concepts of the RV-based MOO to design a ranking strategy for the solutions of a COP. We first transform the COP into a biobjective optimization problem (BOP) and then solve it by using the covariance matrix adaptation evolution strategy (CMA-ES), which is arguably one of the most competitive evolutionary algorithms of current interest. We propose an RV-based ranking strategy to calculate the mean and update the covariance matrix in CMA-ES. Besides, the RV is explicitly tuned during the optimization process based on the characteristics of COPs in a RV-based MOO framework. We also propose a repair mechanism for the infeasible solutions and a restart strategy to facilitate the population to escape from the infeasible region. We test the proposal extensively on two well-known benchmark suites comprised of 36 and 112 test problems (at different scales) from the IEEE CEC (Congress on evolutionary computation) 2010 and 2017 competitions along with a real-world problem related to power flow. Our experimental results suggest that the proposed algorithm can meet or beat several other state-of-the-art constrained optimizers in terms of the performance on a wide variety of problems.

关键词： Optimization Covariance matrices Maintenance engineering Benchmark testing Decision feedback equalizers evolutionary computation Cybernetics Constrained optimization covariance matrix adaptation evolution strategy (CMA-ES) multiobjective optimization (MOO) reference vector-based ranking (RVRanking) strategy

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Multi-system genetic algorithm for complex system optimization

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SOFT COMPUTING 2022年第19期26卷 10187-10205页

作者： Ma, Haiping Shan, Yu Wang, Jinglin Chen, Xiaolei Yang, Zhile Simon, Dan Shaoxing Univ Dept Elect Engn Shaoxing Zhejiang Peoples R China Univ Nottingham Ningbo China Dept Elect & Elect Engn Ningbo Zhejiang Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China Cleveland State Univ Dept Elect & Comp Engn Cleveland OH 44115 USA

Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit parallelism in population-based search algorithms, to solve multiple coupled subsystems simultaneously in a complex system. The proposed MSGA is composed of within-subsystem evolution and cross-subsystem migration operators. The objective of the former is to optimize each subsystem by appropriate search strategies, and the objective of the latter is to exchange information between multiple subsystems by migration, which is based on the similarity probability of objectives and constraints, and the intersection probability of solutions in different subsystems. During migration across subsystems, three statistical approaches of measuring similarity and three metrics of solution intersection in information theory are used to calculate these probabilities. Performance is tested on a set of multi-subsystem benchmark functions, and the simulation results show that cross-subsystem migration plays the key role for the performance of MSGA. Furthermore, the proposed MSGA is compared with other competitive algorithms, and results show that it is a promising multi-system optimization algorithm. In summary, the contribution of this paper is the introduction of multi-system optimization to the EA community.

关键词： evolutionary computation Multi-system genetic algorithm Complex system optimization Multi-objective optimization Migration

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Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage evolutionary Algorithm

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IEEE ACCESS 2022年 10卷 89153-89169页

作者： Verma, Monika Sreejeth, Mini Singh, Madhusudan Babu, Thanikanti Sudhakar Alhelou, Hassan Haes Delhi Technol Univ Dept Elect Engn New Delhi 110042 India Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Tishreen Univ Fac Mech & Elect Engn Dept Elect Power Engn Latakia 2230 Syria

The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local minimum. Aquila optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutation occur to generate a new population. The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.

关键词： Genetic algorithms Statistics Behavioral sciences Chaos evolutionary computation Convergence Metaheuristics Hunting-based algorithm evolutionary algorithm Aquila optimizer (AO) chaotic maps

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Image Deformation Estimation via Multiobjective Optimization

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IEEE ACCESS 2022年 10卷 53307-53323页

作者： Nakane, Takumi Xie, Haoran Zhang, Chao Univ Fukui Dept Engn Fukui 9108507 Japan Japan Adv Inst Sci & Technol Nomi 9231211 Japan

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.

关键词： Strain Estimation Optimization Deformable models computational modeling evolutionary computation Genetic algorithms Genetic algorithm image deformation estimation image registration multi-objective evolutionary algorithm

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evolutionary Dual-Ensemble Class Imbalance Learning for Human Activity Recognition

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2022年第4期6卷 728-739页

作者： Guo, Yinan Chu, Yaoqi Jiao, Botao Cheng, Jian Yu, Zekuan Cui, Ning Ma, Lianbo China Univ Min & Technol Beijing Sch Mech & Informat Engn Beijing 100083 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Coal Res Inst Xuzhou 100083 Jiangsu Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China Northeastern Univ Coll Software Shenyang 110819 Peoples R China

Human activity recognition is an imbalance classification problem in essence since various human actions may occur at different frequencies. Traditional ensemble class imbalance learning methods integrate resampling technique with multi-classifier models, which obtain better generalization than single ones. However, the number of base classifiers is often determined in advance, resulting in the redundant structure of a multi-classifier and larger computational cost. Moreover, various combinations of base classifiers may have similar recognition accuracy, forming a multimodel optimization problem. To address the issue, a dual-ensemble class imbalance learning method is presented, in which two ensemble learning models are nested each other. For each sub-dataset, three heterogeneous sub-classifiers compose of internal ensemble learning model, and the one with the highest recognition accuracy is selected as base classifier. In external ensemble learning model, an optimal combination that contains the smallest number of base classifiers and identifies human actions most exactly is found by multimodal evolutionary algorithms. Based on seven imbalanced activity datasets derived from localization data for person activity, statistical experimental results show that the proposed evolutionary dual-ensemble class imbalance learning method provides the simplest ensemble structure with the best accuracy for imbalanced data that outperforms other five widely-used ensemble classification methods on all metrics.

关键词： Training Data models Learning systems Feature extraction evolutionary computation Costs computational modeling Class imbalance learning ensemble learning under-sampling multi-modal evolutionary algorithm human activity recognition

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DSGA: A Distributed Segment-Based Genetic Algorithm for Multi-Objective Outsourced Database Partitioning

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INFORMATION SCIENCES 2022年 612卷 864-886页

作者： Ge, Yong-Feng Zhan, Zhi-Hui Cao, Jinli Wang, Hua Zhang, Yanchun Lai, Kuei-Kuei Zhang, Jun Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne 3011 Australia South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne 3083 Australia Peng Cheng Lab Shenzhen 518055 Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Chaoyang Univ Technol Dept Business Adm Taichung 413 Taiwan Hanyang Univ Ansan 15588 South Korea

The outsourced distributed database is frequently used to tackle the large amounts of data in various smart city scenarios. The data partition technique is a significant research topic in the outsourced distributed database because it can directly affect the database perfor-mance in data exchange and sharing. Multiple objectives, including communication cost, load balance, and data privacy, should be considered during data partition. Previous approaches remain narrow in dealing with one of these objectives. However, the emphasis on any single objective cannot improve the entire performance of the outsourced dis-tributed database. In this paper, a distributed segment-based genetic algorithm (DSGA) is proposed, which can protect data privacy as well as achieve the trade-off between com-munication cost and load balance. For privacy protection, a digit-based anonymity strategy is proposed based on the characteristic of attributes, which can maintain information integrity and achieve fuzzy identification. After that, a three-layer distributed framework is proposed for the multi-objective optimization to enhance the search efficiency and to achieve the trade-off between communication cost and load balance. Specifically, two segment-based operators, i.e., segment-based recombination and segment-based muta-tion, are proposed to sufficiently exchange evolutionary information to accelerate the con-vergence speed and to maintain population diversity to cover the whole multi-objective space, respectively. The performance of the proposed DSGA is verified in terms of solution accuracy and convergence speed. The effect of proposed strategies and operators in DSGA is also confirmed.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Multi-objective optimization evolutionary computation Database partitioning Distributed genetic algorithm Smart cities

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