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interval many-objective evolutionary algorithm guided by dynamic dual-sequence mechanism

SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Cai, Xingjuan Li, Jinli Ma, Jinlong Zhang, Zhixia Xu, Qingyuan Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing 100190 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

interval many-objective evolutionary algorithms (IMaOEAs) have received significant achievements in recent years. However, it is difficult for the algorithm to quickly select good interval individuals since the uncertainty affects the definition of interval dominance relations. To further reduce the computational burden of uncertainty on the interval optimization process, this paper proposes a dual-sequence mechanism-guided interval many-objective evolutionary algorithm. First, the interval binary R2 evaluation indicator IR2 was designed, which can effectively evaluate the convergence and diversity of interval individuals. Second, an uncertainty dominance relation for interval individuals is proposed and uncertainty is quantified using the weighted LP norm (WLP). Finally, the dynamic dual-sequence (DDS) mechanism was ultimately employed to retain the most exceptional individuals within the population, while simultaneously eliminating those with subpar performance in terms of convergence, diversity, and uncertainty. To extensively evaluate the performance of the proposed approach, 16 benchmark problems were used as the test suite. The experimental results demonstrate that the approach outperforms five advanced interval many-objective evolutionary algorithms, showcasing its superior performance and competitiveness.

关键词： interval many-objective optimization R2 indicator interval uncertainty dominance relationship Dynamic dual-sequence

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An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty

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INFORMATION SCIENCES 2022年 583卷 56-72页

作者： Zhang, Zhixia Zhao, Mengkai Wang, Hui Cui, Zhihua Zhang, Wensheng Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing Peoples R China

Task scheduling is an important research direction in cloud computing. The current research on task scheduling considers mainly the design of scheduling strategies and algorithms and rarely gives attention to the influences of uncertain factors, such as the network bandwidth and millions of instructions per second (MIPS), on the scheduling process. The network bandwidth and MIPS directly affect the performance of a virtual machine (VM), which further influences the scheduling performance. In this paper, uncertain factors are transformed into interval parameters. The make-span, scheduling cost, load balance, and task completion rate are simultaneously considered in the scheduling process. Then, an interval many-objective cloud task scheduling optimization (I-MCTSO) model is designed to simulate real cloud computing task scheduling. To implement this model, an interval many-objective evolutionary algorithm (InMaOEA) is proposed. An interval credibility strategy is employed to improve the convergence performance. The hyper-volume and degree of overlap based on the interval congestion distance strategy are used to increase the population diversity. Simulation results demonstrate the effectiveness and superior performance of InMaOEA in comparision with other algorithms. The proposed approaches can provide decision-makers with an efficient allocation plan for cloud task scheduling. (c) 2021 Elsevier Inc. All rights reserved.

关键词： interval optimization interval many-objective optimization many-objective evolutionary algorithm Cloud task scheduling

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Workflow migration in uncertain edge computing environments based on interval many-objective evolutionary algorithm

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EGYPTIAN INFORMATICS JOURNAL 2023年第4期24卷

作者： Shi, Zhenyu Zhao, Tianhao Li, Qi Zhang, Zhixia Cui, Zhihua Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China

In edge computing (EC), when the edge server (ES) is processing tasks delivered by the mobile devices (MDs), the MDs move outside the coverage of the ES, where task migration is required to ensure service continuity. Most current research on task migration ignores inter-task dependencies and uncertain computing environments, and it focuses mainly on migration scenarios where MDs have a one-to-one or many-to-one relationship with ESs. Aiming at the problem of workflow migration with multi-MDs and multi-ESs in uncertain environments, this paper proposes an interval many-objective optimized workflow migration in uncertain environments (IMaOWMUE) model that considers transforming uncertainty factors into interval parameters for processing, along with the migration delay, maximum completion time, energy consumption, and load balancing as an objective function, and at the same time, utilize real-time priority scheduling strategies to achieve the fast response of the tasks. Considering the dependency of tasks and the changing characteristics of ES load in a migration environment, this paper designs a migration-based interval many-objective evolutionary algorithm (MI-MaOEA), which adopts an interval confidence strategy to improve algorithm convergence and formulates an objective value-dominated hierarchical sorting and dual-migration selection strategy based on the migration delay and the success rate of the migration to improve the diversity of the populations. Simulation results show that MIMaOEA optimizes 27%, 35%, 14%, and 80% in solving the four objective values of I-MaOWMUE, and enables the solution to have faster converse speed and better distribution.

关键词： Edge computing Workflow migration interval many-objective optimization Evolutionary algorithm

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interval many-objective dynamic charging planning in wireless rechargeable sensor networks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第19期36卷 e8150-e8150页

作者： Zhang, Yu Zhao, Tianhao Wu, Linjie Cui, Zhihua Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China

Charging path planning in wireless sensor networks (WSNs) refers to designing an efficient charging path for sensor nodes in the network. However, most charging schemes mainly consider the planning of charging paths and pay little attention to the impact of uncertainties, such as road conditions and environment on the planning of charging paths, as well as ignoring the charging problem of new nodes in need of charging. Road conditions and the environment directly affect the energy consumption of wireless charging vehicles (WCVs) during traveling. To address the aforementioned challenges, this article proposes an interval many-objective charging path scheme model, the WCV consumption is an uncertain value, it will change according to the environment, and road conditions, so we represent it as an interval parameter with upper and lower bounds. An interval high-dimensional multi-objective model with target energy consumption, path distance, number of dead nodes, and communication delay is constructed. Second, to implement this model, an interval SPEA2 algorithm (I-SPEA2) that introduces an environmental response mechanism is proposed. I-SPEA2 treats individual target interval values as ranges of values on a two-dimensional coordinate axis, forming a quadrilateral, calculates individual size probabilities based on the area to determine the dominant relationship, and combines fixed distance and interval overlap to eliminate redundant individuals. The simulation results show that the interval dynamic model is effective in prolonging the lifecycle of WSN as well as the proposed algorithm reduces the mortality rate of the nodes by 15%, 28%, 13%, 16%, and 21% compared with other algorithms.

关键词： dynamic charging path planning interval many-objective optimization SPEA2 wireless rechargeable sensor networks

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Cooperative interference to achieve interval many-objective evolutionary algorithm for association privacy secure computing migration

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期255卷

作者： Cui, Zhihua Shi, Zhenyu Li, Qi Zhao, Tianhao Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Australia

In this paper, we study secure computing migration scenarios in uncertain environments with the presence of multiple malicious eavesdroppers (MEs). Specifically, when edge servers (ESs) execute tasks delivered by smart devices (SDs), SDs may move beyond the coverage of ESs, and computing migration (CM) of unfinished tasks is required to ensure service continuity. There is a risk of privacy leakage during task migration, and MEs use colluding eavesdropping to eavesdrop on the migrated tasks, and we consider eavesdropping on the associated tasks through data sharing among MEs to improve the eavesdropping efficiency. For eavesdropping in MEs, we achieve eavesdropping strikes using cooperative interference by jammers, which benefit by providing jamming services. In addition, uncertain computational scenarios directly affect the efficiency of task execution, and we consider the uncertainty factor in the malicious eavesdropping environment. To this end, this paper proposes the secure computational migration of associative privacy in uncertain environments (SCMAPUE) model, which transforms uncertainties into interval parameters, and optimizes the five objectives of migration delay, maximum completion time, energy consumption, load balancing and migration reliability to achieve efficient task execution and reliable migration. Aiming at the model characteristics, this paper designs an interval many-objective evolutionary algorithm for reliable migration (IMaOEA-RM), which employs a condition-based interval confidence strategy and a multi-access secure migration selection strategy to improve the convergence of the algorithm, and utilizes a dual-migration crossover strategy in order to adjust the jammer partners and improve the population diversity. Simulation results show that our proposed IMaOEA-RM algorithm can provide a more reliable and efficient migration scheme than existing algorithms.

关键词： Mobile edge computing Computing migration Physical layer security(PLS) interval many-objective optimization Evolutionary algorithm

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