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Individual Evaluation Scheduling for Experiment-Based evolutionary multi-objective optimization

ELECTRONICS AND COMMUNICATIONS IN JAPAN 2010年第2期93卷 12-24页

作者： Kaji, Hirotaka Kita, Hajime Kyoto Univ Dept Elect Engn Kyoto 6068501 Japan Kyoto Univ Acad Ctr Comp & Media Studies Kyoto 6068501 Japan

Since the pioneer work on Evolution Strategies, experiment-based optimization is one of the most promising applications of evolutionary computation. Recent progress in automatic control and instrumentation provides us with a smart environment called Hardware in the Loop simulation (HILS) for such applications. However, since optimization through experiment has the severe condition of limited evaluation time and fluctuation of observations, we must develop methodologies that overcome these problems. This paper discusses the application of multi-Objeclive evolutionary Algorithms (MOEAs) to Experiment-Based multi-objective optimization of the parameters of dynamical systems. In such applications, we must apply various parameter candidates spreading near the Pareto frontier to the system, which causes fluctuation of the observed criteria due to the transient response by parameter switching. For reduction of loss time caused by such transient responses in the evaluation of criteria, we propose the techniques called Evaluation Order Scheduling and Evaluation Time Scheduling. Numerical experiments using a formal test problem and experiments in a HILS environment with real internal-combustion engines have demonstrated the effectiveness of the proposed methods. Q 20 10 Wiley Periodicals, Inc. Electron Comm Jpn, 93(2): 12-24, 2010;Published online in Wiley InterScience (www. ***). DOI 10.1002/ecj.10224

关键词： evolutionary multi-objective optimization dynamical system traveling salesman problem engine calibration

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High Performance Computing for Cyber Physical Social Systems by Using evolutionary multi-objective optimization Algorithm

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2020年第1期8卷 20-30页

作者： Wang, Gai-Ge Cai, Xingjuan Cui, Zhihua Min, Geyong Chen, Jinjun Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao Peoples R China China Univ Petr Huadong Coll Comp Qingdao Peoples R China Jiangsu Normal Univ Sch Comp Sci Xuzhou Jiangsu Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun Peoples R China Northeast Normal Univ Sch Comp Changchun Peoples R China Univ Exeter Exeter EX4 Devon England Swinburne Univ Technol Swinburne Data Sci Res Inst Hawthorn Vic 3122 Australia

Cyber-physical social systems (CPSS) is an emerging complicated topic which is a combination of cyberspace, physical space, and social space. Many problems in CPSS can be mathematically modelled as optimization problems, and some of them are multi-objective optimization (MOO) problems (MOPs). In general, the MOPs are difficult to solve by traditional mathematical programming methods. High performance computing with much faster speed is required to address these issues. In this paper, a kind of high performance computing approaches, evolutionary multi-objective optimization (EMO) algorithms, is used to deal with these MOPs. A floorplanning case study is presented to demonstrate the feasibility of our proposed approach. B*-tree and a multistep simulated annealing (MSA) algorithm are cooperatively used to solve this case. As per experimental results for this case, the proposed method is well capable of searching for feasible floorplan solutions, and it can reach 74.44 percent (268/360) success rates for floorplanning problems.

关键词： optimization High performance computing Computational modeling Mathematical model Adaptation models Robot sensing systems Computer architecture Cyber-physical social systems high performance computing evolutionary multi-objective optimization floorplanning multistep simulated annealing

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Benchmarking large-scale subset selection in evolutionary multi-objective optimization

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INFORMATION SCIENCES 2023年第1期622卷 755-770页

作者： Shang, Ke Shu, Tianye Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain inspired Intelligent Shenzhen 518055 Peoples R China

In the field of evolutionary multi-objective optimization (EMO), the standard practice is to present the final population of an EMO algorithm as the output. However, it has been shown that the final population often includes solutions which are dominated by other solutions generated and discarded in previous generations. Recently, a novel EMO framework has been developed to solve this issue by storing all the non-dominated solutions generated during the evolution in an archive, and selecting a subset of solutions from the archive as the output. The key component of this framework is the subset selection from the archive, which typically stores a large number of candidate solutions. However, most relevant studies have focused on small candidate solution sets for environmental selection. There is no benchmark test suite for large-scale subset selection. This study aims to fill this research gap by proposing a benchmark test suite for large-scale subset selection, and providing a comparison between several representative subset selection algorithms using the proposed test suite. The proposed test suite together with the benchmarking studies provides a baseline for researchers to understand, use, compare, and develop large-scale subset selection algorithms in the EMO field.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Large-scale subset selection Benchmarking evolutionary multi-objective optimization Many-objective optimization

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A dominance tree and its application in evolutionary multi-objective optimization

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INFORMATION SCIENCES 2009年第20期179卷 3540-3560页

作者： Shi, Chuan Yan, Zhenyu Lue, Kevin Shi, Zhongzhi Wang, Bai Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software Beijing Peoples R China Univ Virginia Dept Syst & Informat Engn Charlottesville VA 22903 USA Brunel Univ Uxbridge UB8 3PH Middx England Chinese Acad Sci Inst Comp Technol Beijing 100864 Peoples R China

Most contemporary multi-objective evolutionary algorithms (MOEAs) store and handle a population with a linear list, and this may impose high computational complexities on the comparisons of solutions and the fitness assignment processes. This paper presents a data structure for storing the whole population and their dominating information in MOEAs. This structure, called a Dominance Tree (DT), is a binary tree that can effectively and efficiently store three-valued relations (namely dominating, dominated or non-dominated) among vector values. This paper further demonstrates DTs potential applications in evolutionary multi-objective optimization with two cases. The first case utilizes the DT to improve NSGA-II as a fitness assignment strategy. The second case demonstrates a DT-based MOEA (called a DTEA), which is designed by leveraging the favorable properties of the DT. The simulation results show that the DT-improved NSGA-II is significantly faster than NSGA-II. Meanwhile, DTEA is much faster than SPEA2, NSGA-II and an improved version of NSGA-II. On the other hand, in regard to converging to the Pareto optimal front and maintaining the diversity of solutions. DT-improved NSGA-II and DTEA are found to be competitive with NSGA-II and SPEA2. (C) 2009 Elsevier Inc. All rights reserved.

关键词： evolutionary multi-objective optimization evolutionary computation Pareto dominance Fitness assignment Computational complexity

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Ensemble just-in-time learning framework through evolutionary multi-objective optimization for soft sensor development of nonlinear industrial processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 184卷 153-166页

作者： Jin, Huaiping Pan, Bei Chen, Xiangguang Qian, Bin Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Beijing Inst Technol Sch Chem & Chem Engn Beijing 100081 Peoples R China

Just-in-time learning (JIT) has recently gained growing popularity for soft sensor development of nonlinear processes. However, traditional JIT methods aim to pursue a globally optimal learning configuration while ignoring the diversity of JIT learning. Since strong JIT learners are desirable yet difficult to get while weak JIT learners are easy to obtain in real practice, a novel soft sensor modeling framework, referred to as evolutional multi-objective optimization (EMO) based ensemble JIT learning (EMO-EJIT), is proposed. Within this framework, the diversity of JIT learning is created by perturbing the weighted Euclidean distance (WED) based similarity measure. Then diverse and accurate JIT learners equipped with diverse WED similarity measures are generated using an EMO approach. Finally, a stacking strategy is used to achieve ensemble prediction. By employing the proposed modeling framework, EMO based ensemble locally weighted partial least squares (EMO-ELWPLS) and EMO based ensemble JIT extreme learning machine (EMO-EJTTELM) soft sensors are developed. The application results from a simulated fed-batch penicillin fermentation process and an industrial fed-batch chlortetracycline fermentation process have demonstrated the effectiveness and superiority of the proposed method.

关键词： Soft sensor Just-in-time learning Ensemble learning evolutionary multi-objective optimization Locally weighted partial least squares Extreme learning machine

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Association rule hiding based on evolutionary multi-objective optimization

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INTELLIGENT DATA ANALYSIS 2016年第3期20卷 495-514页

作者： Cheng, Peng Lee, Ivan Lin, Chun-Wei Pan, Jeng-Shyang Harbin Inst Technol Shenzhen Grad Sch Shenzhen Guangdong Peoples R China Univ S Australia Sch IT & Math Sci Adelaide SA 5001 Australia Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Fujian Peoples R China Southwest Univ Sch Comp & Informat Sci Chongqing Peoples R China

When data mining techniques are applied to discover useful knowledge behind a large data collection, they are often required to preserve some confidential information, such as sensitive frequent itemsets, rules and so on. A feasible way to ensure the confidentiality is to sanitize the database and conceal sensitive information. However, the sanitization process often produces side effects, thus minimizing these side effects is an important task. An important but ignored fact is that a tradeoff exists within different side effects. When attempting to improve the performance on one dimension, the performance on other dimensions often will be degraded. In this paper, we focus on privacy preserving in association rule mining. Since there is a tradeoff within different side effects, we tried to minimize them from the view of multi-objective optimization. A rule hiding approach based on evolutionary multi-objective optimization (EMO) is proposed. It hides sensitive rules through removing identified items. The side effects on missing non-sensitive rules, ghost rules and data loss are formulated as optimization objectives. EMO is utilized to find a suitable subset of transactions for modification so that side effects can be minimized. Experimental results on real datasets illustrate that the proposed approach can achieve satisfactory results with fewer side effects. In addition, the EMO-based approach can produce multiple hiding solutions in a single run. It provides the opportunity for a user to choose freely the preferred one by preference or experience.

关键词： Privacy preserving data mining association rule hiding evolutionary multi-objective optimization EMO

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Autonomous robot navigation based on the evolutionary multi-objective optimization of potential fields

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ENGINEERING optimization 2013年第1期45卷 19-43页

作者： Herrera Ortiz, Juan Arturo Rodriguez-Vazquez, Katya Padilla Castaneda, Miguel A. Arambula Cosio, Fernando IIMAS UNAM Circuito Escolar Mexico City DF Mexico CCADET UNAM Mexico City DF Mexico

This article presents the application of a new multi-objective evolutionary algorithm called RankMOEA to determine the optimal parameters of an artificial potential field for autonomous navigation of a mobile robot. Autonomous robot navigation is posed as a multi-objective optimization problem with three objectives: minimization of the distance to the goal, maximization of the distance between the robot and the nearest obstacle, and maximization of the distance travelled on each field configuration. Two decision makers were implemented using objective reduction and discrimination in performance trade-off. The performance of RankMOEA is compared with NSGA-II and SPEA2, including both decision makers. Simulation experiments using three different obstacle configurations and 10 different routes were performed using the proposed methodology. RankMOEA clearly outperformed NSGA-II and SPEA2. The robustness of this approach was evaluated with the simulation of different sensor masks and sensor noise. The scheme reported was also combined with the wavefront-propagation algorithm for global path planning.

关键词： genetic algorithms evolutionary multi-objective optimization autonomous robot navigation artificial potential fields

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Physical programming for preference driven evolutionary multi-objective optimization

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APPLIED SOFT COMPUTING 2014年第0期24卷 341-362页

作者： Reynoso-Meza, Gilberto Sanchis, Javier Blasco, Xavier Garcia-Nieto, Sergio Univ Politecn Valencia Inst Univ Automat & Informat Ind Valencia 46022 Spain

Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization. (C) 2014 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization design procedure evolutionary multi-objective optimization Physical programming Many-objective optimization Preference articulation Decision making

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Temporal Information Services in Large-Scale Vehicular Networks Through evolutionary multi-objective optimization

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第1期20卷 218-231页

作者： Dai, Penglin Liu, Kai Feng, Liang Zhang, Haijun Lee, Victor Chung Sing Son, Sang Hyuk Wu, Xiao Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400040 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400040 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Daegu Gyeongbuk Inst Sci & Technol Dept Informat & Commun tion Engn Daegu 42988 South Korea

Temporal information services are critical in implementing emerging intelligent transportation systems. Nevertheless, it is challenging to realize timely temporal data update and dissemination due to an intermittent wireless connection and a limited communication bandwidth in dynamic vehicular networks. Some previous studies have considered the temporal data dissemination in vehicular networks, but they are limited to the service region, which is inside the coverage of roadside units. To enhance system scalability, it is imperative to exploit the synergic effect of vehicle-to-infrastructure (V2I) and vehicle-tovehicle (V2V) communications for providing efficient temporal information services in such an environment. With the above motivations, we propose a novel system architecture to enable efficient data scheduling in hybrid V2I/V2V communications by having the global knowledge of network resources of the system. On this basis, we formulate a temporal data upload and dissemination (TDUD) problem, aiming at optimizing two conflict objectives simultaneously, which are enhancing the data quality and improving the delivery ratio. Furthermore, we propose an evolutionary multi-objective algorithm called MO-TDUD, which consists of a decomposition scheme for han- filing multiple objectives, a scalable chromosome representation for TDUD solution encoding, and an evolutionary operator designed for TDUD solution reproduction. The proposed MO-TDUD can he adaptive to different requirements on data quality and delivery ratio by selecting the best solution from the derived Pareto solutions. Last but not least, we build the simulation model and implement MO-TDUD for performance evaluation. The comprehensive simulation results demonstrate the superiority of the proposed solution.

关键词： Vehicular networks temporal information services evolutionary multi-objective optimization

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A Delaunay Triangulation Based Density Measurement for evolutionary multi-objective optimization 2nd

A Delaunay Triangulation Based Density Measurement for Evolu...

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2nd Australasian Conference on Artificial Life and Computational Intelligence (ACALCI)

作者： Qi, Yutao Yin, Minglei Li, Xiaodong Xidian Univ Sch Comp Sci & Technol Xian Peoples R China RMIT Univ Sch Comp Sci & IT Melbourne Vic Australia

ISBN: (纸本)9783319282701;9783319282695

Diversity preservation is a critical issue in evolutionary multi-objective optimization algorithms (MOEAs), it has significant influence on the quality of final solution set. In this wok, a crowding density measurement is developed for preserving diversity in MOEAs by using the Delaunay triangulation mesh built on the population in the objective space. Base on the property of the Delaunay triangulation, the new density measurement considers both the Euclidean distance and the relative position between individuals, and thus provide a more accurate estimation of the density around a specific individual within the population. Experimental results indicate that the suggested density measurement help to improve the performance of MOEAs significantly.

关键词： evolutionary multi-objective optimization Diversity preservation Delaunay triangulation Density measurement

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