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A Comparative Study of Dispatching Rule Representations in evolutionary Algorithms for the Dynamic Unrelated Machines Environment

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IEEE ACCESS 2022年 10卷 22886-22901页

作者： Planinic, Lucija Backovic, Hrvoje Durasevic, Marko Jakobovic, Domagoj Univ Zagreb Fac Elect Engn & Comp Zagreb 10000 Croatia Visage Technol Zagreb 10000 Croatia

Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time-consuming process, it can be automated by using various machine learning and evolutionary computation methods. In previous research, genetic programming has been the most commonly used method for automatically designing new dispatching rules. However, there are many other evolutionary methods that use representations other than genetic programming that can be used to create dispatching rules. Some, such as gene expression programming, have already been used successfully, while others, such as Cartesian genetic programming or grammatical evolution, have not yet been used to generate dispatching rules. In this paper, six different methods (genetic programming, gene expression programming, Cartesian genetic programming, grammatical evolution, stack representation, and analytic programming) for generating dispatching rules for the unrelated machines environment are tested and the results for various scheduling criteria are analysed. It is also analysed how different individual sizes in the tested methods affect the performance and average size of the generated dispatching rules. The results show that, with the exception of grammatical evolution and analytic programming, all tested methods perform quite similarly, with results depending on the selected scheduling criterion. The results also show that Cartesian genetic programming is the most resistant to the occurrence of bloat and evolves dispatching rules with the smallest average size.

关键词： Dispatching Dynamic scheduling Genetic programming Programming Processor scheduling Schedules evolutionary computation Unrelated machines environment scheduling solution representations dispatching rules

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Network unknown-threat detection based on a generative adversarial network and evolutionary algorithm

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第7期37卷 4307-4328页

作者： Zhou, Jinfei Wu, Zhengdong Xue, Yunhao Li, Minghui Zhou, Di Hangzhou Dianzi Univ Sch Cyberspace Hangzhou Peoples R China Zhejiang Uniview Technol Co Ltd Hangzhou Peoples R China

Currently existing intrusion-detection systems can only meet the needs of the people for defense against the known threat, and lag in the detection of the unknown threat. To solve this problem, this study considers that the character of an unknown threat can evolve from known threats and propose a network unknown-threat detection algorithm intrusion detection method based on generating & evolution (IDM-GE) based on a generation countermeasure network and evolutionary computation. The intrusion detection method based on generating algorithm can balance the data set, make the classifier learn the characteristics of normal traffic and attack traffic more fairly, and increase the diversity of attack traffic distribution by dynamic games. The intrusion detection method based on evolution algorithm can mutate and evolve, and the combination of the generating algorithm and evolutionary algorithm can generalize the features of unknown threats from known threats in a large dynamic range while also improving the detection accuracy of unknown-threat traffic. The experimental results show that the proposed IDM-GE algorithm improves the detection accuracy and recall rate to more than 91% compared with the traditional ResNet algorithm.

关键词： data balance evolutionary computation GAN generalization of threat characteristics unknown-threat detection

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Equivalent Circuit Modeling and Parameter Identification for Lithium-ion Batteries Based on Improved Barnacle Mating Optimizer

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SENSORS AND MATERIALS 2022年第9期34卷 3649-3670页

作者： Li, Jiarong Lin, Cheng-Jian Wang, Haiyu Kan, Jiarong Yancheng Inst Technol Coll Elect Engn Yancheng 224000 Jiangsu Peoples R China Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 41170 Taiwan Natl Taichung Univ Sci & Technol Coll Intelligence Taichung 404 Taiwan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300401 Peoples R China

To address the current problems of accuracy and reliability of the equivalent circuit models for lithium-ion batteries and sensors, in this study we propose an improved barnacle mating optimizer (IBMO) to accurately identify parameters. First, an improved second-order RC model (ISO RC) is derived by segmenting the battery hysteresis voltage and introducing it into the second-order RC model (SO RC model) as a controlled source, which better reflects the dynamic effects of the battery than the SO RC model. Second, the tent chaos mapping, cosine control factor, and Levy flight strategy are introduced to enhance the convergence performance of the IBMO. Its convergence performance is verified by comparison with other existing intelligent algorithms using five single-peak and four multipeak test functions. Finally, the IBMO-ISO RC model is established by using the IBMO as the identification strategy of the ISO RC model in the simulation of two real operating conditions of lithium-ion batteries. The root mean square error of the model under the two operating conditions is only 0.0431 and 0.0483, and the mean absolute percentage error is only 0.38 and 0.56%, outperforming other existing models. The IBMO proposed in this study enables higher model recognition accuracy, making it highly convenient for lithium-ion battery and sensor-related research.

关键词： evolutionary computation parameter identification barnacle mating optimizer lithium-ion battery electric vehicles

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A novel linear representation for evolving matrices

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SOFT COMPUTING 2022年第14期26卷 6645-6657页

作者： Gregor, Connor Ashlock, Daniel Ruz, Gonzalo A. MacKinnon, Duncan Kribs, David Univ Guelph Dept Math & Stat Guelph ON N1G 2W1 Canada Univ Adolfo Ibanez Fac Ingn & Ciencias Av Diagonal Torres 2640 Santiago Chile Ctr Appl Ecol & Sustainabil CAPES Santiago Chile

A number of problems from specifiers for Boolean networks to programs for quantum computers can be encoded as matrices. The paper presents a novel family of linear, generative representations for evolving matrices. The matrices can be general or restricted within special classes of matrices like permutation matrices, Hermitian matrices, or other groups of matrices with particular algebraic properties. These classes include unitary matrices which encode quantum programs. This representation avoids the brittleness that arises in direct representations of matrices and permits the researcher substantial control of the part of matrix space being searched. The representation is demonstrated on a relatively simple matrix problem in automatic content generation as well as Boolean map induction and automatic quantum programming. The automatic content generation problem yields interesting results;the generative matrix representation yields worse fitness but a substantially greater variety of outcomes than a direct encoding, which is acceptable when generating content. The Boolean map experiments extend and confirm results that demonstrate that the generative encoding is superior to a direct encoding for the transition matrix of a Boolean map. The quantum programming results are generally quite good, with poor performance on the simplest problems in two of the families of programming tasks studied. The viability of the new representation for evolutionary matrix induction is well supported.

关键词： evolutionary computation Matrix representation Boolean networks Quantum programs Automatic content generation

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

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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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Interval type-2 fuzzy programming method for risky multicriteria decision-making with heterogeneous relationship

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INFORMATION SCIENCES 2022年 584卷 184-211页

作者： Tang, Guolin Long, Jianpeng Gu, Xiaowei Chiclana, Francisco Liu, Peide Wang, Fubin Shandong Univ Finance & Econ Inst Marine Econ & Management Jinan Shandong Peoples R China Shandong Univ Finance & Econ Sch Management Sci & Engn Jinan Shandong Peoples R China Cent South Univ Sch Automat Changsha Peoples R China Univ Kent Sch Comp Canterbury Kent England De Montfort Univ Sch Comp Sci & Informat Inst Artificial Intelligence IAI Leicester Leics England Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain

We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility val-ues of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the estab-lished model. Accordingly, the ranking order of alternatives is derived using the Banzhaf -based overall perceived utility values. Experimental studies pertaining to investment selec-tion problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Risky multicriteria decision making Heterogeneous relationship evolutionary computation Interval type-2 fuzzy set 2-additive fuzzy measure Regret theory

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Resetting Weight Vectors in MOEA/D for Multiobjective Optimization Problems With Discontinuous Pareto Front

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9770-9783页

作者： Zhang, Chunjiang Gao, Liang Li, Xinyu Shen, Weiming Zhou, Jiajun Tan, Kay Chen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vectors (RWVs) for MOEA/D. When the RWV mechanism is triggered, a classic data clustering algorithm DBSCAN is used to categorize current solutions into several parts. A classic statistical method called principal component analysis (PCA) is used to determine the ideal number of solutions in each part of PF. Thereafter, PCA is used again for each part of PF separately and virtual targeted solutions are generated by linear interpolation methods. Then, the new weight vectors are reset according to the interrelationship between the optimal solutions and the weight vectors under the Tchebycheff decomposition framework. Finally, taking advantage of the current obtained solutions, the new solutions in the decision space are updated via a linear interpolation method. Numerical experiments show that the proposed MOEA/D-RWV can achieve good results for bi-objective and tri-objective optimization problems with discontinuous PF. In addition, the test on a recently proposed MaF benchmark suite demonstrates that MOEA/D-RWV also works for some problems with other complicated characteristics.

关键词： Optimization Principal component analysis evolutionary computation Clustering algorithms Heuristic algorithms Machine learning algorithms Machine learning DBSCAN multiobjective evolutionary algorithm based on decomposition (MOEA D) multiobjective evolutionary algorithm (MOEA) principal component analysis (PCA) weight vectors

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A Survey of Decomposition Based evolutionary Algorithms for Many-Objective Optimization Problems

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IEEE ACCESS 2022年 10卷 72825-72838页

作者： Guo, Xiaofang Xian Technol Univ Sch Sci Xian 710021 Peoples R China

The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different directions. This paper gives a systematic comparison of six different components for decomposition-based algorithms, including framework analysis, weight vector generation scheme, aggregation evaluation function construction, reproduction operator, individual selection and update strategy, and the characteristics and application scope of various algorithms are also analyzed in detail in the survey. Different from previous survey on decomposition-based multi-objective evolutionary algorithms, a more detailed classification and experimental comparison are elaborated in the proposed paper.

关键词： Statistics Sociology evolutionary computation Convergence Shape Optimization Licenses Many-objective decomposition evolutionary algorithm

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Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

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IEEE ACCESS 2022年 10卷 76268-76280页

作者： Darwish, Saad M. Essa, Reham M. Osman, Mohamed A. Ismail, Ahmed A. Univ Alexandria Inst Grad Studies & Res Dept Informat Technol Alexandria 21526 Egypt Higher Inst Management & Informat Technol Dept Informat Syst Kafr Al Sheikh 33511 Egypt Higher Inst Comp & Informat Syst Alexandria 21913 Egypt

Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence. The application of association rule mining in healthcare data has been widespread to this point in time. Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques. When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones. This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist). Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information. However, revealing this information puts it at risk of attack. Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining. In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected. It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database. Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).

关键词： Medical services Data privacy Genetic algorithms Databases Optimization Data integrity Task analysis Privacy-preserving data mining healthcare data evolutionary computation sanitization process

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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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