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dynamic multi-objective evolutionary algorithm for IoT services

APPLIED INTELLIGENCE 2021年第3期51卷 1177-1200页

作者： Fang, Shun-shun Chai, Zheng-yi Li, Ya-lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

The primary goal of the Internet of things(IoT) is to provide people with anywhere services in real life. But intelligent IoT shouldn't only provide services, but also consider how to allocate heterogeneous resources reasonably, which has become a very challenging problem. To obtain the best resource allocation scheme, it is crucial to minimize the service cost and service time. Since the two objectives are contradictory, we have modelled IoT services as a dynamic multi-objective optimization problem. Then a dynamic multi-objective evolutionary algorithm for dynamic IoT services(dMOEA/DI) is proposed. In dMOEA/DI, we have designed operators such as the appropriate encoding method, dynamic detection operator, filtering strategy, differential evolution, and polynomial mutation. Based on the single service strategy and collaborative service strategy, experimental research is performed on the agricultural IoT services with dynamic requests under different distributions. The simulation experimental results prove that dMOEA/DI performs better than the contrasted algorithms on the IoT service optimization problems.

关键词： Agriculture Differential evolution dynamic multi-objective optimization Internet of things multi-objective evolutionary algorithm

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A dynamic multi-objective evolutionary algorithm based on prediction

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第1期10卷 1-15页

作者： Wu, Fei Chen, Jiacheng Wang, Wanliang Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

The dynamic multi-objective optimization problem (DMOP) is a common problem in optimization problems;the main reasons are the objective's conflict and environment changes. In this paper, we provide a prediction approach based on diversity screening and special point prediction (DSSP) to tackle the dynamic optimization issue. First, we introduce a decision variable clustering and screening strategy that clusters the decision space of the non-dominated solution set to find the cluster centroids and then employs a decision variable screening strategy to filter out solutions that have an impact on the distribution of individuals. This approach can broaden the range of dynamic multi-objective optimization algorithms. Second, an approach for predicting special points is suggested. The algorithm's convergence is improved following environmental changes by forecasting the special point tracking Pareto front in the object space. Finally, the forward-looking center points are used to predict the non-dominated solution set and eliminate the useless individuals in the population. The prediction strategy can help the solution set converge while maintaining its diversity, which is compared with the four other state-of-the-art strategies. Our experimental results demonstrate that the proposed algorithm, DSSP, can effectively tackle DMOPs.

关键词： dynamic multi-objective optimization prediction diversity maintenance strategy knee point close-to-boundary point convergence

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Balancing energy consumption and risk of delay in high speed trains: A three-objective real-time eco-driving algorithm with fuzzy parameters

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2018年 95卷 652-678页

作者： Fernandez-Rodriguez, Adrian Fernandez-Cardador, Antonio Cucala, Asuncion P. Comillas Pontifical Univ Inst Res Technol ICAI Sch Engn 23 Alberto Aguilera St Madrid 28015 Spain

Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient. In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays. This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination. The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected. The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service.

关键词： Delay recovery dynamic multi-objective optimization Eco-driving Fuzzy logic High speed railway Simulation

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Solving dynamic multi-objective problems with an evolutionary multi-directional search approach

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KNOWLEDGE-BASED SYSTEMS 2020年 194卷 105175-105175页

作者： Hu, Yaru Ou, Junwei Zheng, Jinhua Zou, Juan Yang, Shengxiang Ruan, Gan Xiangtan Univ Dept Math & Computat Sci Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan 411105 Hunan Peoples R China Hengyang Normal Univ Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

The challenge of solving dynamic multi-objective optimization problems is to effectively and efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this paper proposes a multi-direction search strategy, aimed at finding the dynamic Pareto optimal front and/or Pareto optimal set as quickly and accurately as possible before the next environmental change occurs. The proposed method adopts a multi-directional search approach which mainly includes two parts: an improved local search and a global search. The first part uses individuals from the current population to produce solutions along each decision variables direction within a certain range and updates the population using the generated solutions. As a result, the first strategy enhances the convergence of the population. In part two, individuals are generated in a specific random method along every dimensions orientation in the decision variable space, so as to achieve good diversity as well as guarantee the avoidance of local optimal solutions. The proposed algorithm is measured on several benchmark test suites with various dynamic characteristics and different difficulties. Experimental results show that this algorithm is very competitive in dealing with dynamic multi-objective optimization problems when compared with four state-of-the-art approaches. (C) 2019 Elsevier B.V. All rights reserved.

关键词： dynamic dynamic multi-objective optimization Local search multi-directional search strategy

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On the design of a framework integrating an optimization engine with streaming technologies

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 107卷 538-550页

作者： Barba-Gonzalez, Cristobal Nebro, Antonio J. Benitez-Hidalgo, Antonio Garcia-Nieto, Jose Aldana-Montes, Jose F. Univ Malaga Dept Lenguajes & Ciencias Computat Ada Byron Res Bldg E-29071 Malaga Spain

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Streaming data processing Big data Software framework

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Considering spatiotemporal evolutionary information in dynamic multi-objective optimisation

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023年第0期

作者： Fan, Qinqin Jiang, Min Huang, Wentao Jiang, Qingchao Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Xiamen Univ Sch Informat Xiamen Fujian Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China 1550 Haigang Ave Shanghai 201306 Peoples R China

Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi-objective optimisation (DMO), are challenging since the sampling space is time- and space-varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO. In the present study, a sliding-time-window-based population clustering method (SPC) is proposed to effectively solve dynamic multi-objective optimisation problems (DMOPs). In the SPC, the knowledge is provided by saving historical data in the temporal dimension, and a spectral clustering method is used to divide the saved data into multiple neighbourhood subspaces for preserving population diversity in the spatial dimension. The SPC is incorporated into the RM-MEDA and is compared with other recently proposed state-of-the-art dynamic multi-objective evolutionary algorithms (DMOEAs) on 14 DMOPs introduced in IEEE CEC2018. Simulation results demonstrate that the proposed method is capable of enhancing the tracking performance of the RM-MEDA in the dynamically changing environments. Additionally, the SPC is utilised to solve an actual dynamic multi-objective translation control problem of an immersed tunnel element. Results show that the proposed SPC outperforms the knee point-based transfer learning method in terms of both computational cost and tracking performance.

关键词： dynamic multi-objective optimization dynamic optimization evolutionary computation high-performing computing immersed tunnel element

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A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

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SOFT COMPUTING 2017年第4期21卷 885-906页

作者： Azzouz, Radhia Bechikh, Slim Ben Said, Lamjed Univ Tunis SOIE Lab Tunis Tunisia

In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.

关键词： Change severity sensing dynamic multi-objective optimization Time-changing objective functions Adaptive populationmanagement Memory-based strategy Local search-based strategy Change severity-based strategy

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A Parallel multi-objective Cooperative Co-evolutionary Algorithm with Changing Variables 17

A Parallel Multi-objective Cooperative Co-evolutionary Algor...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Xu, Biao Zhang, Yong Gong, Dun-wei Wang, Ling China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781450349390

multi-objective optimization problems with changing variables are very common in real-world applications. This kind of problems often has a changing Pareto-optimal set and a complex relation among decision variables. In order to rapidly track the time-dependent Pareto-optimal front, we propose a framework of parallel cooperative co-evolution based on dynamically grouping decision variables. Decision variables are first divided into a number of groups using the Spearman rank correlation analysis, with different groups having a weak correlation. Then, a sub-population is employed to optimize decision variables in each group using a traditional multi-objective evolutionary algorithm. The evaluation of a complete solution is fulfilled through the cooperation among sub-populations. We compare the proposed algorithm with three state-of-the-art algorithms by applying them to two modified benchmark optimization problems. Empirical results show that the proposed algorithm is superior to the compared ones.

关键词： dynamic multi-objective optimization parallel changing variables co-evolutionary

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Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms

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Journal of Management Science and Engineering 2022年第1期7卷 48-66页

作者： Feng Liu Kan Fang Jiafu Tang Yong Yin School of Management Science and Engineering Dongbei University of Finance and EconomicsDalian116025China College of Management and Economics Tianjin UniversityTianjin300072China Graduate School of Business Doshisha UniversityKyoto602-8580Japan

Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other Asia *** particular,the rotating seru has been widely implemented,where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing *** rotating seru production problem,which determines the rotating sequence of workers as well as the assembling sequence of jobs,is difficult to solve due to conflicting objectives and dynamic release of customer *** solve this problem,we propose a dynamic multiobjective NSGA-II based memetic ***,to preserve desirable population diversity and improve the searching efficiency,we propose different problem-specific evolutionary ***,we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.

关键词： Cellular manufacturing Assembly Rotating seru dynamic multi-objective optimization Evolutionary algorithms

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A dynamic multi-objective Evolutionary Algorithm for Nontrivial Upper Bounds of Real-Time Tasks in Embedded System Design 4

A Dynamic Multi-Objective Evolutionary Algorithm for Nontriv...

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IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

作者： Xing, Haifeng Zhou, Jiantao Song, Xiaoyu Qi, Ruidong Inner Mongolia Univ Coll Comp Sci Hohhot Inner Mongolia Peoples R China Portland State Univ ECE Dept Portland OR 97207 USA

ISBN: (纸本)9781538693803

In the real-time embedded system, how to schedule more real-time tasks has been a difficult point. The nontrivial upper bound of execution time of the real-time task is the key. The analysis of hit-miss behavior of the instruction cache is a problem for the estimation of the nontrivial upper bound, especially more difficult after using shared set-associative instruction caches. And the computation of results for the dynamic problem is a challenge due to the shortcomings of the classic method ILP. In this paper, we present a model for the prediction of nontrivial upper bound, prove that the prediction of the hit-miss number of the shared set-associative instruction cache is a dynamic multi-objective optimization problem, and design a dynamic multi-objective optimization algorithm for the estimation of solution. Simulation experiments demonstrate the effectiveness of our approach for computing the nontrivial upper bound.

关键词： multi-knapsack nontrivial upper bound dynamic multi-objective optimization shared instruction cache behavior timing analysis

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