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many-objective joint optimization for dependency-aware task offloading and service in mobile

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第5期18卷 1238-1259页

作者： Shi, Xiangyu Zhang, Zhixia Cui, Zhihua Cai, Xingjuan Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependencyaware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub -task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.

关键词： Mobile Edge Computing (MEC) Dependency Aware Task Offloading Service Caching Multi-user Resource Constraint many-objective evolutionary algorithm

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A many-objective optimization algorithm with mutation strategy based on variable classification and elite individual

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SWARM AND evolutionary COMPUTATION 2021年 60卷

作者： Liang, Zhengping Zeng, Jiyu Liu, Ling Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Pengcheng Lab Shenzhen 518055 Peoples R China Shenzhen Univ Shenzhen Inst Artificial Intelligence & Robot Soc SZU Branch Shenzhen 518060 Peoples R China

The current many-objective evolutionary algorithms (MaOEAs) generally adopt the mutation strategies designed for single-objective optimization problems directly. However, these mutation operators usually treat different decision variables without distinction and rarely consider the searching direction, which may easily lead to low search efficiency of the algorithms. To address this issue, a variable classification and elite individual based mutation strategy, namely VCEM, is proposed. It first divides decision variables into two categories, i.e., the convergence-related variables and the diversity-related variables. Then, for each generation, an elite individual with the best convergence and a set of elite individuals with good diversity are selected. The convergence-related variables and diversity related-variables of these two types of elite individuals are used to guide the mutation, respectively. The proposed mutation strategy is applied to develop a new algorithm, which is then compared with seven state-of-the-art MaOEAs on a number of benchmark problems. The experimental results demonstrate that the proposed algorithm is more competitive than the compared algorithms. Moreover, VCEM is applied to four different algorithms to compare with the original algorithms, and it is also compared with five mutation operators based on a classical MaOEA. The results further verify the effectiveness and generality of VCEM.

关键词： many-objective optimization many-objective evolutionary algorithm Variable classification Elite individual Mutation strategy

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many-objective joint optimization of computation offloading and service caching in mobile edge computing

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SIMULATION MODELLING PRACTICE AND THEORY 2024年 133卷

作者： Cui, Zhihua Shi, Xiangyu Zhang, Zhixia 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 State Key Lab Intelligent Control & Management Com Inst Automat Beijing 100190 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many -objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non -dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k -nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k -nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many -objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained.

关键词： Edge computing Computation offloading Service caching many-objective evolutionary algorithm

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An efficient many objective optimization algorithm with few parameters

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SWARM AND evolutionary COMPUTATION 2023年 83卷

作者： Zhang, Qingquan Liu, Jialin Yao, Xin 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 Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT England

During the past two decades, numerous many-objective optimization evolutionary algorithms (MaOEAs) have been proposed to tackle the challenges traditional multi-objective evolutionary algorithms face, that is to deal with abundant non-dominated solutions and low selection pressure. Specifically, a series of sophisticated selection strategies have been adopted, some of which need additional pre-defined parameters or a significant amount of extra computation time, which retards their applications in real life. In this paper, we propose an efficient indicator-based MaOEA with few parameters, SDE+-MOEA, that uses an adaptive combination of commonly used selection methods to improve search efficiency based on SDE+. SDE+ addresses situations where SDE cannot distinguish individuals with the same SDE values. The adaptive selection method dynamically selects between one-time and iterative selection methods at different stages of evolution to improve the search efficiency. Furthermore, apart from the four parameters for generating offspring, our proposed SDE+MOEA does not introduce additional parameters. We conduct experimental studies to compare SDE+-MOEA with 11 state-of-the-art algorithms, including 2REA, ISDE+, 0-DEA, LMPFE, CVEA3, SRA, MOEA/DD, IBEA, Two_Arch2, NSGA-III, and SPEA2SDE using four representative performance indicators (HV, SP, PD and GD) on MaF benchmark with 5, 8, 10, and 15 objectives. Experimental studies demonstrate that, compared to the algorithms, SDE+-MOEA achieves better HV and competitive convergence and uniformity performance, while requiring few parameters. It means that SDE+-MOEA can find a solution set with better convergence and uniformity to help decision-makers understand the solved problems. Furthermore, SDE+-MOEA loses little spreadability since a better convergence often leads to a worse spread.

关键词： many-objective evolutionary algorithm Indicator-based selection Multi-criteria decision-making many-objective optimization Multi-objective optimization

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A many-objective optimization based intelligent algorithm for virtual machine migration in mobile edge computing

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第23期35卷

作者： Fan, Tian Guo, Wanwan Zhang, Zhixia Cui, Zhihua Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Peoples R China

With the rapid development of big data, the explosive growth of data promotes the progress of the Internet of Things (IoT). Because it is hard for traditional cloud computing to meet vast computing tasks, scholars propose mobile edge computing (MEC) for the IoT. However, the mobility of users results in the instability of MEC performance. Besides, the conflict of interest between users and service providers needs to be balanced. To solve these problems, this paper constructs a virtual machine migration model based on many-objective optimization (MaOVMMM). In MaOVMMM, four objectives are considered simultaneously: communication expense, computing expense, delay, and energy consumption. A many-objective evolutionary algorithm with double population confrontation (MaOEA-DPC) is suggested to support the MaOVMMM that is proposed. First, the population confrontation strategy is designed to better simulate the relationship between users and service providers. Second, the dynamic probability integration selection strategy is used to ensure the evolution ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of MaOEA-DPC when compared with other algorithms. This proposed approach can provide a superior virtual machine migration scheme for decision-makers.

关键词： many-objective evolutionary algorithm many-objective optimization mobile edge computing virtual machine migration

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The global evaluation strategy for many-objective partial collaborative computation offloading problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第2期35卷

作者： Xue, Zhaoyu Guo, Wanwan Cui, Zhihua Zhang, Wensheng Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan Peoples R China Chinese Acad Sci Inst Automation State Key Lab Intelligent Control & Management Com Beijing Peoples R China

With the number of services expanding in the Internet of Things (IoT), the limited resources of user terminals are insufficient to satisfy the computation needs of all running services. Therefore, we design a collaborative computation-offloading model (CCOM) composed of multiple servers and task offloading modes to solve the problem by offloading tasks from resource-constrained terminals to other computing entities, which achieves the following four objectives: model execution time minimization, task execution time minimization, energy consumption minimization, and most efficient device workload. And the global evaluation strategy based on angle and distance is proposed to address the individual selection problems caused by the overly localized Pareto dominance relationship and objective conflicts in the many-objective evolutionary algorithms. In simulations, the strategy achieves the best Inverted Generation Distance (IGD) performance on 22 benchmark test problems based on Wilcoxon's rank sum statistical test and improves performance in the four objectives above by 18%, 31%, 20%, and 42%, respectively. Finally, we think that the strategy can provide adequate offloading performance for decision-makers.

关键词： computation offloading edge computing many-objective evolutionary algorithm

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A universal large-scale many-objective optimization framework based on cultural learning

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Wang, Xia Ge, Hongwei Zhang, Naiqiang Hou, Yaqing Sun, Liang Dalian Univ Technol Sch Comp Sci & Technol Dalian 116023 Peoples R China Washington Univ St Louis Dept Comp Sci & Engn St Louis MO 63130 USA

When solving large-scale many-objective optimization problems (LMaOPs), due to the large number of variables and objectives involved, the algorithm is faced with a very high-dimensional and complex search space, which is difficult to be explored with limited resources. To address these issues, this paper proposes a universal large-scale many-objective optimization framework based on cultural learning (UCLMO). First, a universal framework is proposed, and multi-objective optimizers can be embedded into the framework to accelerate the convergence. Moreover, inspired by cultural learning, an individual selection strategy based on historical knowledge is proposed to promote the diversity of the population, and an assisted evolution strategy based on normative knowledge is presented to accelerate the convergence of the algorithm. Experiments have been conducted on multi-objective knapsack problems and LMaOPs with decision variables ranging from 500 to 1500, and the number of objectives ranging from 5 to 15. The experimental results verify the superiority and competitiveness of the proposed UCLMO framework in solving LMaOPs compared with state-of-the-art algorithms. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： many-objective evolutionary algorithm Cultural algorithm Large-scale Diversity

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Efficient parasitic-aware hybrid sizing methodology for analog and RF integrated circuits

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INTEGRATION-THE VLSI JOURNAL 2018年 62卷 301-313页

作者： Liao, Tuotian Zhang, Lihong Mem Univ Newfoundland Fac Engn & Appl Sci Dept Elect & Comp Engn St John NF Canada

In this paper, a highly efficient parasitic-aware hybrid sizing methodology is proposed. It involves geometric programming (GP) as the first phase, both single-objective and many-objective evolutionary algorithms (EA) as the second sizing phase. The circuit performance constraints and layout-induced parasitics are considered simultaneously right from the GP sizing phase, while the optimization accuracy is significantly improved in the EA sizing phase. The proposed methodology features an effective integration of layout information into both sizing phases. It has been used to optimize several high-performance analog and RF circuits in different CMOS technologies. The experimental results demonstrate high efficacy of our proposed parasitic-aware hybrid sizing methodology.

关键词： Circuit sizing many-objective evolutionary algorithm Floorplan optimization Geometric programming Parasitic modeling

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Efficient Parasitic-aware gm/ID-based Hybrid Sizing Methodology for Analog and RF Integrated Circuits

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ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS 2021年第2期26卷 1–31页

作者： Liao, Tuotian Zhang, Lihong Mem Univ Newfoundland Fac Engn & Appl Sci Dept Elect & Comp Engn St John NF A1B 3X5 Canada

As the primary second-order effect, parasitic issues have to be seriously addressed when synthesizing high-performance analog and RF integrated circuits (ICs). In this article, a two-phase hybrid sizing methodology for analog and RF ICs is proposed to take into account parasitic effect in the early design stage. It involves symbolic modeling and mixed-integer nonlinear programming (MINLP) in the first phase, and a many-objective evolutionary algorithm (many-OEA)-based sizing refiner in the second phase. With the aid of our proposed current density factor and piecewise curve fitting technique, the g(m)/I-D concept, which is typically utilized to solve the analog circuit design problem, can provide theoretical support to our accurate symbolic modeling. Thus, the intrinsic and interconnect parasitics can be accurately considered in our work with moderate modeling effort. A variety of electrical, geometric, and parasitic (including parasitic mismatch) constraints can be conveniently integrated into our MINLP problem formulation. Moreover, numerical simulations are embedded into the many-OEA-based sizing phase, which is able to tackle floorplan co-optimization. With such dynamic floorplan variation, the parasitics accuracy can be sustained along the evolution. The experimental results demonstrate high efficacy of our proposed parasitic-aware hybrid sizing methodology.

关键词： Analog and RF ICs circuit sizing circuit modeling floorplan optimization g(m)/I-D many-objective evolutionary algorithm mixed-integer nonlinear programming parasitic modeling

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A Cyber-Physical Systems Approach to Optimizing Internet of Vehicles Architecture with Rapidly Evolving Technology 1

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4th International Conference on Internet of Vehicles (IOV)

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

ISBN: (数字)9783319723297

ISBN: (纸本)9783319723297;9783319723280

There is significant work underway to make the internet of vehicles a reality, however the required infrastructure is substantial and the technologies will quickly evolve during the early adaption phase. While an internet of vehicles architecture must be optimized for its key performance metrics, because of the technological volatility, additional objectives measuring the sensitivity of the key performance metrics to technological change should also be included. This would minimize the impact of evolving technologies although it would also effectively double the number of objectives. A large number of objectives creates a significant difficulty for optimization due to Pareto breakdown where a large percentage of the solution space is non-dominated. An approach is developed that casts this as a cyber-physical systems architecting problem using a meta-architecture that represents the problem in terms of systems and interfaces. The systems are formally characterized in manner allowing the objectives to be calculated given an architecture which is a particular instance of the meta-architecture. The meta-architecture is structured as a chromosome to allow the use of a many-objective evolutionary algorithm to optimize the architecture despite the large number of objectives present. An evolutionary algorithm was chosen because it does not require a continuous solution space and can readily enforce constraints on solutions. A guide to problem formulation and models of representative objectives and feasibility constraints are provided to demonstrate this approach.

关键词： Internet of vehicles IOV Cyber-physical system CPS many-objective evolutionary algorithm MaOEA Architecture Optimization

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