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

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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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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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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A Bi-Search evolutionary Algorithm for High-Dimensional Bi-Objective Feature Selection

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2024年第5期8卷 3489-3502页

作者： Xu, Hang Xue, Bing Zhang, Mengjie Putian Univ Sch Mech Elect & Informat Engn Putian 351100 Peoples R China Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

High dimensionality often challenges the efficiency and accuracy of a classifier, while evolutionary feature selection is an effective method for data preprocessing and dimensionality reduction. However, with the exponential expansion of search space along with the increase of features, traditional evolutionary feature selection methods could still find it difficult to search for optimal or near optimal solutions in the large-scale search space. To overcome the above issue, in this paper, we propose a bi-search evolutionary algorithm (termed BSEA) for tackling high-dimensional feature selection in classification, with two contradictory optimizing objectives (i.e., minimizing both selected features and classification errors). In BSEA, a bi-search evolutionary mode combining the forward and backward searching tasks is adopted to enhance the search ability in the large-scale search space;in addition, an adaptive feature analysis mechanism is also designed to the explore promising features for efficiently reproducing more diverse offspring. In the experiments, BSEA is comprehensively compared with 9 most recent or classic state-of-the-art MOEAs on a series of 11 high-dimensional datasets with no less than 2000 features. The empirical results suggest that BSEA generally performs the best on most of the datasets in terms of all performance metrics, along with high computational efficiency, while each of its essential components can take positive effect on boosting the search ability and together make the best contribution.

关键词： Feature extraction Task analysis Search problems Optimization Multitasking evolutionary computation Vectors Bi-search evolutionary mode bi-objective feature selection high-dimensional datasets large-scale search space

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A Multipopulation evolutionary Algorithm Using New Cooperative Mechanism for Solving Multiobjective Problems With Multiconstraint

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IEEE TRANSACTIONS ON evolutionary computation 2024年第1期28卷 267-280页

作者： Zou, Juan Sun, Ruiqing Liu, Yuan Hu, Yaru Yang, Shengxiang Zheng, Jinhua Li, Ke Xiangtan Univ Hunan Engn Res Ctr Intelligent Syst Optimizat & Se Key Lab Intelligent Comp & Informat Proc Minist Educ China Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Secu Xiangtan 411105 Hunan Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England Univ Exeter Dept Comp Sci Exeter EX4 4QF England

In science and engineering, multiobjective optimization problems (MOPs) usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This article aims to solve the challenges brought by multiple complex constraints. First, this article analyzes the relationship between single-constrained Pareto front (SCPF) and their common Pareto front (PF) subconstrained PF (SubCPF). Next, we discussed the SCPF, SubCPF, and unconstraint PF (UPF)'s help to solve constraining PF (CPF). Then, further discusses what kind of cooperation should be used between multiple populations constrained multiobjective optimization algorithm (CMOEA) to better deal with multiconstrained MOPs (mCMOPs). At the same time, based on the discussion in this article, we propose a new multipopulation CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed activation dormancy detection (ADD) to accelerate the optimization process and uses the proposed combine occasion detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.

关键词： Statistics Sociology Optimization Convergence Sun evolutionary computation Computer science Coevolutionary algorithm constrained multiobjective optimization constraint handling

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PEGA: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第6期54卷 3638-3651页

作者： Zhao, Bowen Chen, Wei-Neng Wei, Feng-Feng Liu, Ximeng Pei, Qingqi Zhang, Jun Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Fujian Peoples R China Xidian Univ Sch Telecommun Engn Xian 710126 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300071 Peoples R China Hanyang Univ ERICA Sch Elect & Engn Ansan 15588 South Korea

EA, such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement evolutionary algorithms (EAs) for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solu

关键词： Combinatorial optimization evolutionary computation ECaaS privacy protection secure computing

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