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A many-objective evolutionary algorithm for solving computation offloading problems under uncertain communication conditions

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COMPUTER COMMUNICATIONS 2024年 213卷 22-32页

作者： Li, Qi Shi, Zhenyu Xue, Zhaoyu Cui, Zhihua Xu, Yubin Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China

Computation offloading is a critically important technology in the field of edge computing, enabling improved conditions for task computation. Existing studies on computation offloading largely focus on cooperative offloading and resource allocation. The impact of factors such as uncertainty during task transmission and the different task characteristics on the offloading location is ignored. To address the above issues, this paper designs a many-objective optimization computation offloading model (MaOCO) under uncertainty to cope with offloading requirements. The model formulates channel transmission level strategies and considers five objectives: latency, cost, energy consumption, load balancing, and user satisfaction. A many-objective evolutionary algorithm with deviation selection (MaOEA-DS) is designed to obtain effective offloading strategies. In the environment selection, the deviation is calculated using the concept of variance to choose solutions closer to the origin for the last layer, which further improves the convergence of the algorithm. The simulation results show that MaOEA-DS outperforms other evolutionary algorithms in IGD, GD, SP, and HV.

关键词： Edge Computing many-objective evolutionary algorithm Task offloading Uncertainty

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A many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism

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SWARM AND evolutionary COMPUTATION 2024年 90卷

作者： Yang, Lei Cao, Jiale Li, Kangshun Zhang, Yuanye Xu, Rui Li, Ke South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

In many-objective optimization, both convergence and diversity are equally important. However, in high- dimensional spaces, traditional decomposition-based many-objective evolutionary algorithms struggle to ensure population diversity. Conversely, traditional Pareto dominance-based many-objective evolutionary algorithms face challenges in ensuring population convergence. In this paper, we propose a novel many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism (MaOEAIH) for effectively addressing the difficulty in balancing convergence and diversity. First, we use the concept of interaction force to simulate the convergence (akin to gravity) and diversity (repulsion) of the population. Subsequently, we design an optimization mechanism that combines decomposition and Pareto dominance to enhance the convergence and diversity of the population separately. Simultaneously, to eliminate dominance resistance solutions, we propose a quartile method based on boundary solutions. Additionally, Random perturbations are also introduced to certain individuals within the population to facilitate their escape from local optima. MaOEAIH is compared with some state-of-the-art algorithms on 31 well-known test problems with 3-15 objectives. The experimental results show that, compared to other algorithms, MaOEAIH not only obtains solution sets of higher quality when dealing with different types of many-objective optimization problems, but also effectively addresses key challenges including insufficient selection pressure, difficulty balancing convergence and diversity, and susceptibility to population entrapment in local optima within many-objective optimization scenarios.

关键词： many-objective evolutionary algorithm Interaction force Hybrid evolutionary mechanism Dominance resistance solutions Perturbation

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many-objective evolutionary algorithm based on three-way decision

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EGYPTIAN INFORMATICS JOURNAL 2023年第3期24卷

作者： Cui, Zhihua Li, Bingting Lan, Zhuoxuan Xu, Yubin Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China

In recent years, many-objective optimization problems have been widely used. however, with the increase of the number of objectives, the difficulty of solving increases exponentially, and the imbalance between convergence and diversity becomes more serious. In view of the above problems, this paper combines the idea of three-way decision, redesigns the environment selection strategy, and proposes a many-objective optimization algorithm based on three-way decision. Firstly, the distance from the individual to the ideal point is used as an index to measure individual convergence, the minimum distance from the individual to other solutions is used as an indicator to measure individual diversity, and the individuals with good convergence and good diversity are selected separately by combining the thresholds of the three-way decision;and secondly, A dynamic threshold acquisition method is designed to further improve the performance of the algorithm;Finally, it is proved that the algorithm can effectively balance convergence and diversity through tests of different data sets, so as to verify the feasibility and effectiveness of the algorithm & COPY;2023 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： many-objective optimization problems many-objective evolutionary algorithm Three-way decision Convergence Diversity

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Two-stage adaptive weight vector design method for decomposition based many-objective evolutionary algorithm

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2022年第1-2期39卷 58-69页

作者： Guo, Xiaofang Wang, Yuping Gao, Xiaozhi Xian Technol Univ Sch Sci Xian Shaanxi Peoples R China Univ Eastern Finland Sch Comp Kuopio Finland

In order to acquire the searching information of the irregular shape of the PF accurately, this paper proposes a two-stage adaptive weight vector design approach for decomposition based many objective evolutionary algorithms. Firstly, a preset initial weight vector generation strategy based on the crowding information is proposed to capture the effective area of the distribution of population. Next, the self-organised mapping (SOM) weight vector design method is adopted to generate weight vectors to capture the topological structure of the distribution of PF. In addition, a new composite aggregation function combined Lp metric distance with angle-distance is proposed to improve both convergence and diversity. The performance of our algorithm is examined using a total of 10 problems with both regular and irregular PF. The experimental results show that the proposed method can significantly improve the convergence and diversity performance with irregular shape of PF.

关键词： many-objective evolutionary algorithm two stage crowding self-organised mapping SOM Lp-metric

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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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A many-objective evolutionary algorithm with Spatial Division and Angle Culling Strategy

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Chinese Journal of Electronics 2021年第3期30卷 437-443页

作者： WANG Hongbo YANG Fan TIAN Kena TU Xuyan School of Computer and Communication Engineering Beijing Key Lab.of Knowledge Engineering for Materials ScienceUniversity of Science and Technology Beijing

In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a many-objective evolutionary algorithm based on a spatial division and angle-culling strategy(Ma OEA-SDAC) is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed Ma OEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of Ma OEA-SDAC in solving high-dimensional optimization problems has been verified.

关键词： reasonable balance many-objective evolutionary algorithm convergence environment selection stage angle culling strategy elimination strategy spatial division rich diversity Pareto optimisation angle-culling strategy diversity imbalance restricted matching selection specific project evolutionary computation fast convergence reorganization stage MaOEA-SDAC space division optimal solutions

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An enhanced diversity indicator-based many-objective evolutionary algorithm with shape-conforming convergence metric

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Cao, Jiale Yang, Lei Li, Kangshun Zhang, Yuanye Tian, Jinglin Wang, Dongya South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England

As the number of objectives increases, many-objective optimization problems (MaOPs) become increasingly complex. Traditional indicator-based many-objective evolutionary algorithms can often ensure the convergence of the population but tend to struggle with maintaining its diversity. In this paper, an enhanced diversity indicator-based many-objective evolutionary algorithm with shape-conforming convergence metric, namely, MaOEA-DISC, is presented to relieve this weakness. In MaOEA-DISC, firstly, we focus on the inter-individual spacing relationships within the population based on I-& varepsilon;+ Indicator, proposing a novel enhanced diversity I-& varepsilon;+ Indicator to ensure the enhancement of population diversity while converging. Secondly, we propose a new metric of individual convergence, which calculates the convergence of individuals based on the shape of Pareto front, reducing the errors caused by the shape of the Pareto front when measuring individual convergence, thereby assessing individual convergence more accurately. Finally, to further improve the convergence speed of the population, different mating strategies are employed for mating in the parental generation. MaOEA-DISC is compared with other algorithms on various benchmark MaOPs ranging from 3 to 10 objectives, as well as a real-world MaOPs. Experimental results demonstrate that when dealing with MaOPs, MaOEA-DISC not only achieves excellent population convergence and diversity but also effectively maintains a balance between them, showing promising practical value.

关键词： many-objective evolutionary algorithm Enhanced diversity I indicator Shape-conforming convergence metric Mating strategy

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A many-objective evolutionary algorithm based on constraints for collaborative computation offloading

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SWARM AND evolutionary COMPUTATION 2023年 77卷

作者： Cui, Zhihua Xue, Zhaoyu Fan, Tian Cai, Xingjuan Zhang, Wensheng Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing Peoples R China

This paper designs a many-objective optimized edge and terminal collaborative computation offloading (MaOETCCO) model to give full play to the performance advantages of collaboration and resource sharing be-tween edge server (ES) and intelligent terminal equipment (ITE) for computation offloading, which considers three offloading levels and four objectives: offloading delay minimization, application execution time minimi-zation, energy consumption minimization, and most efficient load balancing. At the same time, a many-objective evolutionary algorithm with load constraints (MaOEA-LC) is designed to solve the model by obtaining Pareto optimal solutions. It consists of a two-layer tournament mating selection based on load constraints, an envi-ronment selection method based on objective value domination, and an evolutionary method of double popu-lation crossover and multipoint mutation. In simulations, the values of the convergence performance evaluation index GD and diversity performance evaluation index Spa are the best in the comparison with other evolutionary algorithms. With optimization of MaOEA-LC, the MaOETCCO model is solved faster with more diverse solutions, and the objective values in the model have been optimized by 31%, 8%, 16%, and 30%, respectively. In addition, the rank value and adjusted p-value of the Friedman statistical test are used to prove the significant advantage of MaOEA-LC.

关键词： Load balancing many-objective evolutionary algorithm Collaborative computation offloading Edge computing

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A many-objective evolutionary algorithm based on integrated strategy for skin cancer detection

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第1期16卷 80-96页

作者： Lan, Yang Xie, Lijie Cai, Xingjuan Wang, Lifang Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China

Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

关键词： integrated strategy intelligent medicine many-objective evolutionary algorithm skin cancer

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Communication-efficient federated recommendation model based on many-objective evolutionary algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Cui, Zhihua Wen, Jie Lan, Yang Zhang, Zhixia Cai, Jianghui Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China

The federated recommendation system (FedRS), which is the application of the recommendation system (RS) in federated learning, has been creatively developed as increasing attention has been paid to user privacy protection. However, traditional federated learning consumes excessive communication time and resource, which seriously limits the development of the FedRS. To reduce the communication cost and improve the recommendation performance of FedRS, an improved many-objective federated recommendation model with a novel parameter reduction strategy is proposed in this paper. The model aims to optimize the number of parameters shared between the server and client to improve communication efficiency in the federated process. Optimal parameter selection solutions can be obtained using the many-objective evolutionary algorithm (MaOEA), which can optimize the recommendation accuracy, novelty, diversity, and communication efficiency of FedRS simultaneously. Furthermore, the reference vector guided evolutionary algorithm (RVEA) was adopted to evaluate the proposed model. Experiments were performed on two famous recommendation datasets to examine the superiority of RVEA for the evaluation. The performance results indicated that the proposed model can not only provide accurate, diverse, and novel recommendations for the client, but can also realize efficient communication.

关键词： Federated learning Recommendation system Federated recommendation model many-objective evolutionary algorithm RVEA

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