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Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

IEEE TRANSACTIONS ON evolutionary computation 2023年第4期27卷 964-979页

作者： Wang, Zi-Jia Jian, Jun-Rong Zhan, Zhi-Hui Li, Yun Kwong, Sam Zhang, Jun Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cyb Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China i4AI Ltd London WCIN3AX England City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Zhejiang Normal Univ Jinhua 321004 Peoples R China Hanyang Univ Dept Elect Engn Ansan 15588 South Korea

Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this article introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in the candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.

关键词： Differential evolution (DE) evolutionary computation gene targeting (GT) large-scale optimization simple and efficient

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A Bi-Objective Knowledge Transfer Framework for evolutionary Many-Task Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2023年第5期27卷 1514-1528页

作者： Jiang, Yi Zhan, Zhi-Hui Tan, Kay Chen Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Hanyang Univ Ansan 15588 South Korea

Many-task optimization problem (MaTOP) is a kind of challenging multitask optimization problem with more than three tasks. Two significant issues in solving MaTOPs are measuring intertask similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the intertask similarity because the intertask similarity is a concept with multiple different aspects. To address this limitation, this article proposes a bi-objective knowledge transfer (BoKT) framework, which aims first to accurately measure different types of intertask similarity using two different measurements and second to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure intertask similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of intertask similarity. We compare the BoKT framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world MaTOPs with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.

关键词： Bi-objective evolutionary computation evolutionary many-task optimization (EMaTO) evolutionary multitask optimization (EMTO) knowledge transfer

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Anomalous variable-length subsequence detection in time series: mathematical formulation and a novel evolutionary algorithm based on clustering and swarm intelligence

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APPLIED INTELLIGENCE 2023年第24期53卷 29585-29603页

作者： Sutrisno, Hendri Phoa, Frederick Kin Hing Natl Dong Hwa Univ Sch Management Dept Informat Management 1 Sec 2Da Hsueh Rd Shoufeng 974301 Hualien Taiwan Acad Sinica Inst Stat Sci 128 Acad RdSect 2 Taipei 11529 Taiwan

Variable-length anomalous subsequence detection in time series has many important applications in the real world, yet the methods presented in existing studies are computationally expensive, as the detection techniques are mostly brute-force approaches. In this work, we formalize the detection problem into a subsequence segmentation problem (SSP) optimization task, in which the time series is segmented by a set of cutting points into subsequences with minimized total distances to the representative motif. The anomalous subsequences can then be accurately located by reducing the dissimilarity among all subsequences, and this technique, when compared to existing techniques, can reduce the number of comparisons required for search. We further introduce a new clustering-based and swarm intelligence-based evolutionary algorithm (CBSI) in this work to solve the highly complex SSP efficiently. The proposed method balances the scopes of exploration and exploitation under a local-global search strategy. The CBSI clusters the solutions in the search space into groups, allowing frequent information sharing among solutions in the same cluster for their exploitation within their own search spaces. Furthermore, the best local solutions are promoted by the global-search strategy to explore the remaining search regions. Through a comparison with existing state-of-the-art techniques in solving both synthetic and real-world problems, we show that any optimization methods under our proposed SSP bring significant computational savings and comparable searching accuracy compared to existing techniques for the detection task. Our proposed CBSI also has the highest searching capability compared to existing and related optimization methods. The experimental results also highlight the scalability of our study to longer time series, larger anomaly sizes and wider search ranges.

关键词： Anomaly detection Data mining Anomalous subsequence discovery evolutionary computation Knowledge discovery Time series analysis

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Model Compression Based on Differentiable Network Channel Pruning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第12期34卷 10203-10212页

作者： Zheng, Yu-Jie Chen, Si-Bao Ding, Chris H. Q. Luo, Bin Anhui Univ IMIS Lab Anhui Prov Anhui Prov Key Lab Multimodal Cognit Computat MOE Key Lab Intelligent Comp & Signal Proc ICSP Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Texas Arlington Dept Comp Sci & Engn Arlington TX 76019 USA

Although neural networks have achieved great success in various fields, applications on mobile devices are limited by the computational and storage costs required for large models. The model compression (neural network pruning) technology can significantly reduce network parameters and improve computational efficiency. In this article, we propose a differentiable network channel pruning (DNCP) method for model compression. Unlike existing methods that require sampling and evaluation of a large number of substructures, our method can efficiently search for optimal substructure that meets resource constraints (e.g., FLOPs) through gradient descent. Specifically, we assign a learnable probability to each possible number of channels in each layer of the network, relax the selection of a particular number of channels to a softmax over all possible numbers of channels, and optimize the learnable probability in an end-to-end manner through gradient descent. After the network parameters are optimized, we prune the network according to the learnable probability to obtain the optimal substructure. To demonstrate the effectiveness and efficiency of DNCP, experiments are conducted with ResNet and MobileNet V2 on CIFAR, Tiny ImageNet, and ImageNet datasets.

关键词： computational modeling Training Network architecture Neural networks evolutionary computation Computer architecture Image coding Channel pruning convolutional neural network differentiable method model compression neural network pruning

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A novel evolutionary algorithm inspired from triangle search and its applications on parameters identification of photovoltaic models

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SOFT COMPUTING 2023年第20期27卷 14835-14860页

作者： Wei, Zhenglei Zhou, Huan Cen, Fei Xie, Lei Zhu, Wenqiang Zhang, Peng Hao, Qinzhi China Aerodynam Res & Dev Ctr Mianyang 621013 Sichuan Peoples R China Air Force Engn Univ Inst Aeronaut Engn Xian 710038 Shaanxi Peoples R China

Parameters identification of photovoltaic (PV) models is significant for forecasting power of PV system and simulating the PV models. To accurately identify the parameters of different PV models based on measured current-voltage characteristics, a novel algorithm inspired by triangle vertex and edge, called triangle search optimization (TSO), is proposed in the paper. The TSO algorithm is divided into two phases: the triangle vertex searching (TVS) and triangle edge searching (TES) phases. In the TVS phase, the population is divided into two subpopulations, which can be enhanced by vertex searching operators for exploration and the covariance matrix adaptation evolution strategy (CMA-ES) for exploitation. In the TES phase, the differential evolution vector between superior and inferior solutions is employed to improve the diversity of the population. The experiments on CEC 2017 test suite show that the proposed TSO performs better than the state-of-the-art algorithms in convergence accuracy. The novel algorithm, TSO, is employed to solve the parameters identification problems of single-diode PV model, double-diode PV model and PV module. Comprehensive experiments indicate that TSO can obtain a highly competitive performance compared with other state-of-the-art algorithms, especially in terms of accuracy and robustness.

关键词： Triangle search optimization evolutionary computation PV model parameters identification CEC 2017

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Solution of constrained mixed-integer multi-objective optimal power flow problem considering the hybrid multi-objective evolutionary algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第1期17卷 66-90页

作者： Ali, Aamir Abbas, Ghulam Keerio, Muhammad Usman Koondhar, Mohsin Ali Chandni, Kiran Mirsaeidi, Sohrab Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah Pakistan Southeast Univ Sch Elect Engn Nanjing Peoples R China Beijing Jiaotong Univ Sch Elect Engn Beijing Peoples R China

An Optimal power flow (OPF) is non-linear and constrained multi-objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two-phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi-objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300-bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best-compromised solution is obtained by using fuzzy decision-making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30-bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints.

关键词： OPF problem 30-bus test system solar power generation Optimisation techniques Wind power plants uniformly distributed Pareto front Solar power stations and photovoltaic power systems Pareto optimisation solar power global Pareto front wind power generation evolutionary computation conventional generators constrained mixed-integer multiobjective optimal power flow problem fuzzy decision-making technique Nonlinear and functional equations (numerical analysis) IEEE 30 bus network evolutionary algorithms probability distribution functions hybrid two-phase algorithm IEEE 300-bus network wind power plants uncertain renewable energy resources hybrid multiobjective evolutionary problem integer programming probability parameter less constraint technique load flow fuzzy set theory nonlinear equations wind power nonlinear model global PF decision making hyper volume indicator technique IEEE standards multiobjective EAs load demand highly complex problems subject computational complexity Combinatorial mathematics hybrid power systems

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A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第2期59卷 2041-2049页

作者： Wang, Zhongju Wang, Long Huang, Chao Luo, Xiong Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528399 Peoples R China

A hybrid multi-modal ensemble learning model is proposed for short-term solar irradiance forecasting based on historical observations and sky images in this paper. In the proposed model, historical observations of solar irradiance are utilized to extract temporal characteristics, and ground-based sky images are used as an exogenous input to offer cloud cover information. A powerful ensemble learning model, Extreme Gradient Boosting (XGBoost), is employed to capture the function relationships between input features and future observations. Since mean squared error loss is sensitive to the extreme large or small historical irradiance, a novel loss function is proposed to improve robustness of XGBoost. In order to find out the best controlling parameters, Rao-1 algorithm is employed for its easy operation. To validate performance of the proposed method, a solar irradiance dataset containing three-year historical observations and ground-based sky images collected from Folsom is employed. Meanwhile, five commonly applied methods, LASSO, Ridge regression, support vector regression, boosted regression trees, the generic XGBoost, are considered as benchmarking methods. The forecasting horizons from 5 to 30 min are considered for all compared methods while two metrics, mean absolute error and root mean squared error, are computed. Experimental results prove that the proposed hybrid model has better forecasting performance compared with benchmarking methods over all forecasting horizons.

关键词： Ensemble learning evolutionary computation forecasting solar irradiance

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Large Wind Farm Layout Optimization Using Nature Inspired Meta-Heuristic Algorithms

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IETE JOURNAL OF RESEARCH 2023年第5期69卷 2683-2700页

作者： Aggarwal, Sanjeev K. Saini, Lalit M. Sood, Vijay Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Patiala Punjab India Natl Inst Technol Elect Engn Dept Kurukshetra Haryana India Ontario Tech Univ Elect Engn Dept Oshawa ON Canada

Wind farm layout optimization (WFLOP) is a complex constrained optimization problem. In this paper, an application of biogeography based optimization (BBO) algorithm for large WFLOP is presented and its performance is compared against three other major meta-heuristic algorithms namely genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). The performance is evaluated on the basis of a modified cost of energy fitness function across 25 different wind farm scenarios. Further statistical inference has been drawn on the basis of 99% Confidence Intervals and Student's t-test which shows that the fitness function values obtained using BBO are better when compared to the other algorithms for the same number of iterations. The execution time in case of PSO is largest, while GA takes the lowest amount of time.

关键词： BBO evolutionary computation GA large wind farm layout optimization PSO statistical inference

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A novel multimodal multi-objective optimization algorithm for multi-robot task allocation

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年

作者： Miao, Zhenhua Huang, Wentao Jiang, Qingchao Fan, Qinqin Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Minist Educ China Key Lab Power Transmiss & Power Convers Control Beijing Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China Shanghai Maritime Univ Logist Res Ctr 1550 Haigang Ave Shanghai 201306 Peoples R China

Multi-robot task allocation (MRTA) is widely used in various fields and plays an important role in some complex task environments due to its ability to distribute parallel processing tasks. However, the multi-robot cooperative system is susceptible to actual environments or preferences of decision-makers. Therefore, providing enough solutions/schemes in the MRTA is important. To improve the reliability and feasibility of obtained solution set, an improved multimodal multi-objective differential evolution algorithm hybrid with a simulated annealing algorithm (IMMODE-SA) is proposed to solve MRTA problems in this study. In the proposed IMMODE-SA, a novel population initialization method is used to improve the population quality, and a redundant solution deletion method is employed to delete redundant solutions during the search process. Moreover, a simulated annealing algorithm is utilized to improve the exploitation capability in the last generation of evolutionary process. To verify the performance of the proposed algorithm, extensive simulation experiments are conducted on three MRTA instances. Experimental results show that the proposed algorithm performs better than other competitors on MRTA instances in terms of Hypervolume (HV). Also, the validity of the proposed algorithm is demonstrated via three experiments and experimental analysis results indicate that the IMMODE-SA can provide more equivalent optimal schemes to decision makers. Finally, it is crucial to solve MRTA problems with time window constraints.

关键词： Multi-robot task allocation multi-robot cooperation path planning multimodal multi-objective optimization evolutionary computation

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Contribution-Based Cooperative Co-Evolution With Adaptive Population Diversity for Large-Scale Global Optimization [Research Frontier]

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IEEE computationAL INTELLIGENCE MAGAZINE 2023年第3期18卷 56-68页

作者： Yang, Ming Gao, Jie Zhou, Aimin Li, Changhe Yao, Xin China Univ Geosci Wuhan Peoples R China East China Normal Univ Shanghai Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China

Cooperative co-evolution (CC) is an evolutionary algorithm that adopts the divide-and-conquer strategy to solve large-scale optimization problems. It is difficult for CC to specify a suitable subpopulation size to solve different subproblems. The population diversity may be insufficient to search for the global optimum during subpopulations' evolution. In this paper, an adaptive method for enhancing population diversity is embedded in a contribution-based CC. In CC, there are two kinds of subpopulation: the convergent or stagnant subpopulations and the non-convergent and non-stagnant subpopulations. A method is proposed in the paper to evaluate the convergent and stagnant subpopulations' contributions to improving the best overall objective value, which is different from the contribution evaluation on the non-convergent and non-stagnant subpopulations. In each co-evolutionary cycle, the new CC adaptively determines to select a subpopulation, which can make a greater contribution to improving the best overall objective value, between the above two kinds of subpopulation to undergo evolution. When a convergent or stagnant subpopulation is selected to undergo evolution, the subpopulation is re-diversified to enhance its global search capability. Our experimental results and analysis suggest that the new CC algorithm can improve the performance of CC and serves as a competitive solver for large-scale optimization problems.

关键词： Uniform resource locators Source coding Sociology Tutorials evolutionary computation Benchmark testing Statistics

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