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many-objective cloud manufacturing service selection and scheduling with an evolutionary algorithm based on adaptive environment selection strategy

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APPLIED SOFT COMPUTING 2021年 112卷 107737-107737页

作者： Wang, Tianri Zhang, Pengzhi Liu, Juan Zhang, Minmin Taiyuan Univ Technol Sch Econ & Management Taiyuan 030024 Peoples R China Taiyuan Univ Technol Postgrad Educ Innovat Ctr Big Data Management & A Taiyuan 030024 Peoples R China

Cloud manufacturing service selection and scheduling (CMSSS) problem has obtained wide attentions in recent years. However, most existing methods describe this problem as single-, bi-, or tri-objective models. Little work deals with this problem in four or more objectives simultaneously. This paper investigated CMSSS problem in consideration of the interests of users, cloud platform and service providers. An eight-objective CMSSS optimization model is constructed for the problem. Meanwhile, a many-objective evolutionary algorithm with adaptive environment selection (MaOEA-AES) is designed to address the problem. Specifically, diversity-based population partition technology is used to divide the population into multiple subregions to maintain the population diversity, and an adaptive penalty boundary intersection (APBI) distance is designed to select elitist solutions in different stages of evolutionary process. The proposed algorithm is tested on 2 cases with 5 and 8 objectives in CMSSS problems and each of them has sixteen experimental groups with different problem scales. The experiment results show that MaOEA-AES is competitive to resolve the MaO-CMSSS model compared with eight state-of-the-art evolutionary algorithms in convergence and diversity. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Cloud manufacturing many-objective evolutionary algorithm Manufacturing service scheduling Adaptive penalty boundary intersection

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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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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 e7474-e7474页

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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Improving many-objective evolutionary algorithms by Means of Edge-Rotated Cones 16th

Improving Many-Objective Evolutionary Algorithms by Means of...

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Wang, Yali Deutz, Andre Back, Thomas Emmerich, Michael Leiden Univ Leiden Inst Adv Comp Sci Niels Bohrweg 1 NL-2333 CA Leiden Netherlands

ISBN: (纸本)9783030581145;9783030581152

Given a point in m-dimensional objective space, any e-ball of a point can be partitioned into the incomparable, the dominated and dominating region. The ratio between the size of the incomparable region, and the dominated (and dominating) region decreases proportionally to 1/2(m-1), i.e., the volume of the Pareto dominating orthant as compared to all other volumes. Due to this reason, it gets increasingly unlikely that dominating points can be found by random, isotropic mutations. As a remedy to stagnation of search in many objective optimization, in this paper, we suggest to enhance the Pareto dominance order by involving an obtuse convex dominance cone in the convergence phase of an evolutionary optimization algorithm. We propose edge-rotated cones as generalizations of Pareto dominance cones for which the opening angle can be controlled by a single parameter only. The approach is integrated in several state-of-the-art multi-objective evolutionary algorithms (MOEAs) and tested on benchmark problems with four, five, six and eight objectives. Computational experiments demonstrate the ability of these edge-rotated cones to improve the performance of MOEAs on many-objective optimization problems.

关键词： Cone order Pareto dominance many-objective evolutionary algorithm

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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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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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Explicable recommendation based on knowledge graph

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期200卷 1页

作者： Cai, Xingjuan Xie, Lijie Tian, Rui Cui, Zhihua Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligent Lab Taiyuan Peoples R China

Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users. To work out the problem, a model of explainable recommendation on account of knowledge graph as well as many-objective evolutionary algorithm is proposed, which combines recommendation and explanation. In this work, embedding vectors obtained by embedding based method are used to quantify the explainability, so as to obtain the explainability of paths between users and items. Candidate recommendation list of users is gained from constructed knowledge graph. many-objective evolutionary algorithm is used to optimize the list of candidate recommendation so as to seek a set of tradeoff solutions to the four objective functions of accuracy, diversity, novelty and explainability. Then, the best path among object user and recommended items is chosen in knowledge graph as the explanation. Finally, the conclusion that can be drawn from various experiments is that the presented model can boost explainability without reducing the precision, diversity as well as novelty.

关键词： Explainable recommendation Knowledge graph Unified method Recommendation system many-objective evolutionary algorithm

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many-objective optimization of feature selection based on two-level particle cooperation

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INFORMATION SCIENCES 2020年 532卷 91-109页

作者： Zhou, Yu Kang, Junhao Guo, Hainan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Guangdong Lab Artificial Intelligence & Digital E Shenzhen Peoples R China Shenzhen Univ Coll Management Shenzhen Peoples R China

Feature selection (FS) plays a crucial role in classification, which aims to remove redundant and irrelevant data *** However, for high-dimensional FS problems, Pareto optimal solutions are usually sparse, signifying that most of the decision variables are zero. Solving such problems using most existing evolutionary algorithms is difficult. In this paper, we reformulate FS as a many-objective optimization problem comprising three objectives to be minimized. To solve this problem, we proposed a binary particle swarm optimization with a two-level particle cooperation strategy. In the first level, to maintain rapid convergence, randomly generated ordinary particles and strict particles filtered by ReliefF are combined as the initialized particles. In the second level, under the decomposition multiobjective optimization framework, cooperation between particles is conducted during the update process to search for Pareto solutions more efficiently. In addition, a many-objective reset operation is proposed to enable the algorithm to jump out of the local optimum. Experimental studies on 10 real-world benchmark data sets revealed that our proposed algorithm could effectively reduce the number of features and achieve a competitive classification accuracy compared with some state-of-the-art evolutionary FS methods and non-evolutionary approaches. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Feature selection Particle swarm optimization many-objective evolutionary algorithm ReliefF Pareto optimal solutions

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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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