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Enhanced feature combinational optimization for multivariate time series based dynamic early warning in power systems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期252卷

作者： Xu, Jian Jiang, Xinxiong Liao, Siyang Ke, Deping Sun, Yuanzhang Yao, Liangzhong Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

Multivariate time series (MTS) learning-based dynamic early warning (DEW) shows promising potential in preventing power systems from underlying risks. However, as a fundamental and essential step for constructing DEW models, performing efficient feature selection (FS) within high-dimensional candidate features (HDCF) of power grids is challenging, especially considering the MTS format and security-dominated characteristics of electric utilities. This paper proposes an enhanced feature combinational optimization (FCO) method to select beneficial variables for MTS-based DEW models within the HDCF of power grids. First, to identify features that contain valuable temporal patterns for DEW, a wrapper FCO framework combined MTS learning and evolutionary computation is designed. Then, to improve the FCO efficiency and solution quality when facing HDCF, a maximal information coefficient (MIC) based enhancement scheme is developed, semi-guiding the FCO initialization and evolution. Finally, by integrating MIC and iterative search, hybrid end-to-end FS is achieved. Experiments on the synthetic data from the IEEE 39-bus system and the real-world data acquired from a provincial power grid in China show that, via the MIC semi-guidance, the proposed method possesses higher FCO efficiency and better results within HDCF compared to wrapper benchmarks. Compared to filter benchmarks, it is more compatible with supervised MTS and can consider the practical performance of features by interacting with the embedded learning models. Results also verify that, with the features from the proposed method, more accurate DEW models can be obtained to predict the target event in early stages.

关键词： Power systems Dynamic early warning Combinational optimization Multivariate time series evolutionary computation

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Ordered Submodularity-Based Value Maximization of UAV Data Collection in Earthquake Areas

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4886-4897页

作者： Zhang, Jixian Wang, Zhemin Wu, Hao Li, Weidong Yunnan Univ Sch Informat Sci Engn Kunming 650504 Peoples R China Yunnan Univ Yunnan Key Lab Intelligent Syst Kunming 650504 Peoples R China Yunnan Univ Sch Math Stat Kunming Peoples R China

UAV data collection problems have been a popular research topic in many fields, and the use of UAV-assisted rescue in earthquake-affected areas can significantly increase the rescue success rate. In this paper, we model the UAV data collection problem for earthquake disaster areas based on the mobile crowdsensing point of interest (POI) model, with the goal of maximizing the value of the collected data, and we transform the problem into an integer programming model with ordered submodularity properties and constraints. Additionally, we design an evolutionary algorithm (Max-UDC) to solve the problem;this algorithm iteratively computes new UAV sequences and eliminates inferior sequences using mutation to obtain a sequence of length K with the maximum data value. Through theoretical analysis and experimental proof, we demonstrate that the Max-UDC algorithm can obtain an approximation ratio related to forward curvature and backward curvature with an expected number of iterations. In the experimental section, the superior performance of Max-UDC is shown by comparing it with the OPT, G-Greedy, and Greedy algorithms.

关键词： Autonomous aerial vehicles Data collection Crowdsensing Data models Earthquakes evolutionary computation Task analysis evolutionary algorithm maximization problem mobile crowdsensing ordered submodularity unmanned aerial vehicle

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evolutionary Retrosynthetic Route Planning [Research Frontier]

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IEEE computationAL INTELLIGENCE MAGAZINE 2024年第3期19卷 58-72页

作者： Zhang, Yan He, Xiao Gao, Shuanhu Zhou, Aimin Hao, Hao East China Normal Univ Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of Big Data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable tool for molecular retrosynthesis. At present, Monte Carlo tree search is a mainstream search framework employed to address this problem. Nevertheless, its search efficiency is compromised by its large search space. Therefore, this paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization, marking the first use of evolutionary Algorithm (EA) in the field of multi-step retrosynthesis. The proposed method involves modeling the retrosynthetic problem into an optimization problem, defining the search space and operators. Additionally, to improve the search efficiency, a parallel strategy is implemented. The new approach is applied to four case products and compared with Monte Carlo tree search. The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53.9%. The time required to search three solutions decreases by an average of 83.9%, and the number of feasible search routes increases by 1.38 times. The source code is available at https://***/ilog-ecnu/EvoRRP.

关键词： Monte Carlo methods Source coding evolutionary computation Manuals Machine learning Search problems Planning Chemistry Optimization methods Big Data Molecular biology Synthesizers

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BitEA: BitVertex evolutionary Algorithm to Enhance Performance for Register Allocation

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IEEE ACCESS 2024年 12卷 115497-115514页

作者： Terci, Gizem Sungu Abdulhalik, Enes Bayrakci, Alp Arslan Boz, Betul Gebze Tech Univ Comp Engn Dept TR-41400 Kocaeli Turkiye Marmara Univ Comp Engn Dept TR-34854 Istanbul Turkiye

Register allocation is important in compiler design to enhance the execution time of programs, as CPU registers operate significantly faster than memory locations. The challenge of optimally assigning program variables to a limited number of CPU registers requires innovative solutions within constrained timeframes, particularly when traditional methods are not applicable due to computational infeasibility on current CPU architectures. This study introduces BitVertex evolutionary Algorithm (BitEA), a novel approach grounded in bitwise operations and bit-based solutions, designed to accelerate computational performance in register allocation. Our experimental results show that BitEA outperforms the existing methods by a factor of up to 60 across all tested scenarios in the DIMACS benchmarks. Furthermore, in terms of solution quality, BitEA achieves lower chromatic numbers on 9 DIMACS benchmarks compared to its closest contemporaries. This research underscores that BitEA has potential to set a new standard for register allocation through its superior speed and solution quality.

关键词： Registers Resource management evolutionary computation Color Statistics Computer architecture Program processors evolutionary optimization parallel processing register allocation graph coloring problem vertex-weighted graphs crossover operator evolutionary algorithms k-coloring BitVertex representation bitwise operations

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Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 4444-4458页

作者： Sun, Lingchen Liang, Jie Liu, Shuaizheng Yong, Hongwei Zhang, Lei Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China OPPO Res Inst Shenzhen 518000 Peoples R China

High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to achieve these goals by minimizing the corresponding yet conflicting losses, such as the & ell;(1) loss and the adversarial loss. Unfortunately, the commonly used gradient-based optimizers, such as Adam, are hard to balance these objectives due to the opposite gradient decent directions of the contradictory losses. In this paper, we formulate the perception-distortion trade-off in SR as a multi-objective optimization problem and develop a new optimizer by integrating the gradient-free evolutionary algorithm (EA) with gradient-based Adam, where EA and Adam focus on the divergence and convergence of the optimization directions respectively. As a result, a population of optimal models with different perception-distortion preferences is obtained. We then design a fusion network to merge these models into a single stronger one for an effective perception-distortion trade-off. Experiments demonstrate that with the same backbone network, the perception-distortion balanced SR model trained by our method can achieve better perceptual quality than its competitors while attaining better reconstruction fidelity. Codes and models can be found at https://***/csslc/EA-Adam .

关键词： Optimization Indexes Distortion Loss measurement Task analysis evolutionary computation Distortion measurement Super-resolution GAN evolutionary algorithm optimization

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An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics

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ALGORITHMICA 2024年第1期86卷 1-32页

作者： Bambury, Henry Bultel, Antoine Doerr, Benjamin Inst Polytech Paris Ecole Polytech Palaiseau France Inst Polytech Paris Lab Informat LIX Ecole Polytech CNRS Palaiseau France

Jump functions are the most-studied non-unimodal benchmark in the theory of randomized search heuristics, in particular, evolutionary algorithms (EAs). They have significantly improved our understanding of how EAs escape from local optima. However, their particular structure-to leave the local optimum one can only jump directly to the global optimum-raises the question of how representative such results are. For this reason, we propose an extended class JumP(k,delta) of jump functions that contain a valley of low fitness of width delta starting at distance k from the global optimum. We prove that several previous results extend to this more general class: for all k <= n(1/3)/ln n and delta < k, the optimal mutation rate for the (1 + 1) EA is delta/n, and the fast (1 + 1) EA runs faster than the classical (1 + 1) EA by a factor super-exponential in delta. However, we also observe that some known results do not generalize: the randomized local search algorithm with stagnation detection, which is faster than the fast (1 + 1) EA by a factor polynomial in k on JumP(k), is slower by a factor polynomial in n on some JumP(k,delta) instances. computationally, the new class allows experiments with wider fitness valleys, especially when they lie further away from the global optimum.

关键词： evolutionary computation Benchmarks Runtime analysis Theory

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STUBCODER: Automated Generation and Repair of Stub Code for Mock Objects

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第1期33卷 1-31页

作者： Zhu, Hengcheng Wei, Lili Terragni, Valerio Liu, Yepang Cheung, Shing-Chi Wu, Jiarong Sheng, Qin Zhang, Bing Song, Lihong Hong Kong Univ Sci & Technol Hong Kong Peoples R China McGill Univ Montreal PQ Canada Univ Auckland Auckland New Zealand Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Dept Comp Sci & Engn Shenzhen Peoples R China WeBank Co Ltd Shenzhen Peoples R China

Mocking is an essential unit testing technique for isolating the class under test from its dependencies. Developers often leverage mocking frameworks to develop stub code that specifies the behaviors of mock objects. However, developing and maintaining stub code is labor-intensive and error-prone. In this article, we present StubCoder to automatically generate and repair stub code for regression testing. StubCoder implements a novel evolutionary algorithm that synthesizes test-passing stub code guided by the runtime behavior of test cases. We evaluated our proposed approach on 59 test cases from 13 open source projects. Our evaluation results show that StubCoder can effectively generate stub code for incomplete test cases without stub code and repair obsolete test cases with broken stub code.

关键词： Software testing mocking test generation and repair genetic programming evolutionary computation program analysis

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Higher Order Knowledge Transfer for Dynamic Community Detection With Great Changes

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IEEE TRANSACTIONS ON evolutionary computation 2024年第1期28卷 90-104页

作者： Ma, Huixin Wu, Kai Wang, Handing Liu, Jing Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change occurs;namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This article focuses on dynamic community detection with substantial changes by integrating higher order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher order knowledge transfer strategy is designed to determine first-order and higher order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.

关键词： Heuristic algorithms Optimization Knowledge transfer Proposals Linear programming Task analysis evolutionary computation Dynamic community detection (DCD) evolutionary algorithm (EA) multiobjective optimization transfer learning

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A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Feng, Zhong-kai Zhang, Li Mo, Li Wang, Yong-qiang Niu, Wen-jing Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Changjiang Water Resources Commiss Changjiang River Sci Res Inst Wuhan 430010 Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China

With growing attention focused on energy generation and environmental conservation, the operation of cascade reservoirs that considers power generation benefits and ecological flow requirements plays an increasingly important role in water resource and power systems. However, the traditional optimization methods may fail to solve complex cascade reservoirs operation models with strong spatial -temporal coupling physical constraints. To effectively address this problem, this study proposes an efficient multi -objective cooperation search algorithm (MOCSA) that utilizes the modified team communication, reflective learning operator and internal competition operators to enhance global exploration and local exploitation. MOCSA is tested on a group of multi -objective benchmark functions and real -world constrained engineering problems. The results demonstrate that MOCSA can provide better results for most benchmark test functions in comparison with the competitive algorithms. Besides, MOCSA exhibits excellent search ability to find feasible solutions of constrained engineering problems. The proposed method is then applied to resolve a real -world reservoir system under different operation scenarios. Simulations indicate that MOCSA provides diversified decision options for the operation of cascade reservoir system and find a more diverse set of non -dominated solutions within the feasible solution space. Overall, this research presents MOCSA as an effective multi -objective optimization tool for complex cascade reservoir operation problems.

关键词： Multireservoir operation Multi -objective optimization Ecological protection Generation benefit evolutionary computation Engineering Optimization

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Multi-Objective Archiving

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IEEE TRANSACTIONS ON evolutionary computation 2024年第3期28卷 696-717页

作者： Li, Miqing Lopez-Ibanez, Manuel Yao, Xin Univ Birmingham Sch Comp Sci Birmingham B15 2TT England Univ Manchester Alliance Manchester Business Sch Manchester M13 9SS England Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Most multiobjective optimization algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e.g., as the population in evolutionary computation). Over the last two decades, archiving, the process of comparing new solutions with previous ones and deciding how to update the archive/population, stands as an important issue in evolutionary multiobjective optimization (EMO). This is evidenced by constant efforts from the community on developing various effective archiving methods, ranging from conventional Pareto-based methods to more recent indicator-based and decomposition-based ones. However, the focus of these efforts is on empirical performance comparison in terms of specific quality indicators;there is lack of systematic study of archiving methods from a general theoretical perspective. In this article, we attempt to conduct a systematic overview of multiobjective archiving, in the hope of paving the way to understand archiving algorithms from a holistic perspective of theory and practice, and more importantly providing a guidance on how to design theoretically desirable and practically useful archiving algorithms. In doing so, we also present that archiving algorithms based on weakly Pareto-compliant indicators (e.g., $\epsilon $ -indicator and IGD+), as long as designed properly, can achieve the same theoretical desirables as archivers based on Pareto-compliant indicators (e.g., hypervolume indicator). Such desirables include the property limit-optimal, the limit form of the possible optimal property that a bounded archiving algorithm can have with respect to the most general form of superiority between solution sets.

关键词： Optimization Statistics Sociology Pareto optimization Computer science Systematics History Archive archiving methods environmental selection evolutionary computation multiobjective optimization population update

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