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Preference-based Pareto optimization in certain and noisy environments

ENGINEERING OPTIMIZATION 2009年第1期41卷 23-38页

作者： Trautmann, Heike Mehnen, Joern TU Dortmund Univ Stat Fac D-44221 Dortmund Germany Cranfield Univ Decis Engn Ctr Cranfield MK43 0AL Beds England

In this article a method for including a priori preferences of decision makers into multicriteria optimization problems is presented. A set of Pareto-optimal solutions is determined via desirability functions of the objectives which reveal experts' preferences regarding different objective regions. An application to noisy objective functions is not straightforward but very relevant for practical applications. Two approaches are introduced in order to handle the respective uncertainties by means of the proposed preference-based Pareto optimization. By applying the methods to the original and uncertain Binh problem and a noisy single cut turning cost optimization problem, these approaches prove to be very effective in focusing on different parts of the Pareto front of the ori-ginal problem in both certain and noisy environments.

关键词： multiobjective evolutionary optimization multiobjective evolutionary algorithm noisy environment desirability function single cut turning problem

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DRAL: a tool for discovering relevant e-activities for learners

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KNOWLEDGE AND INFORMATION SYSTEMS 2013年第1期36卷 211-250页

作者： Zafra, Amelia Romero, Cristobal Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

Web-based educational systems routinely collect vast quantities of data on students' e-activity generating log files that offer researchers unique opportunities to apply data mining techniques and discover interesting information to improve the learning process. This paper proposes a friendly and intuitive tool called DRAL to detect the most relevant e-activities that a student needs to pass a course based on features extracted from logged data in an education web-based system. The method uses a more flexible representation of the available information based on multiple instance learning to prevent the appearance of a great number of missing values and is based on a multi-objective grammar guided genetic programming algorithm which obtains simple and clear classification rules which are markedly useful to identify the number, type and time of e-activities more relevant so that a student has a high probability to pass a course. To validate this approach, our proposal is compared with the most traditional proposals in multiple instance learning over the years. Experimental results demonstrate that the approach proposed successfully improves the accuracy of previous models by finding a balance between specificity and sensitivity values.

关键词： Web usage mining Educational data mining Multiple instance learning multiobjective evolutionary algorithm Genetic programming

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Robotic Disassembly Sequence Planning With Backup Actions

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第3期19卷 2095-2107页

作者： Laili, Yuanjun Li, Xiang Wang, Yongjing Ren, Lei Wang, Xiaokang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Univ Birmingham Sch Engn Birmingham B15 2TT W Midlands England St Francis Xavier Univ Dept Comp Sci Antigonish NS B2G 2W5 Canada

A key step in remanufacturing is disassembly of the ``core'' or the returned product to be remanufactured. Disassembly sequence planning is challenging due to uncertainties in the conditions of the cores. Rust, corrosion, deformation, and missing parts may require disassembly plans to be changed and adapted frequently. Conventional industrial automation that usually serves in repetitive and structured activities may fail when it is applied to disassembly. This research investigates the flexible sequencing of robotic disassembly in the presence of failed automation operations and develops online recovery by incorporating backup actions. It starts with modeling the time and success rate of a backup action. The expected disassembly time and completion rate of a disassembly plan are deduced according to the failure probability of both the operations and their backup actions. A biobjective optimization model for robotic disassembly sequence planning is established using a dual-selection multiobjective evolutionary algorithm. Two solution selection criteria are combined to produce potential offspring candidates in each evolutionary generation. Experimental results show that the backup actions allow efficient recovery from automation and can potentially improve the robustness of robotic disassembly.

关键词： Planning Interference Uncertainty Service robots Automation Tools Robustness Backup actions multiobjective evolutionary algorithm robotic disassembly sequence planning

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Detection and prediction of segments containing extreme significant wave heights

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OCEAN ENGINEERING 2017年 142卷 268-279页

作者： Duran-Rosal, A. M. Fernandez, J. C. Gutierrez, P. A. Hervas-Martinez, C. Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg3rd Floor E-14071 Cordoba Spain

This paper presents a methodology for the detection and prediction of Segments containing very high Significant Wave Height (SSWH) values in oceans. This kind of prediction is needed in order to account for potential changes in a long-term future operational environment of marine and coastal structures. The methodology firstly characterizes the wave height time series by approximating it using a sequence of labeled segments, and then a binary classifier is trained to predict the occurrence of SSWH periods based on past height values. A genetic algorithm (GA) combined with a likelihood-based local search is proposed for the first stage (detection), and the second stage (prediction) is tackled by an Artificial Neural Network (ANN) trained with a multiobjective evolutionary algorithm (MOEA). Given the unbalanced nature of the dataset (SSWH are rarer than non SSWH), the MOEA is specifically designed to obtain a balance between global accuracy and individual sensitivities for both classes. The results obtained show that the GA is able to group SSWH in a specific cluster of segments and that the MOEA obtains ANN models able to perform an acceptable prediction of these SSWH.

关键词： Time series segmentation multiobjective evolutionary algorithm Local search Prediction Detection Extreme significant wave height Minimum sensitivity

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Optimal design and seismic performance of Multi-Tuned Mass Damper Inerter (MTMDI) applied to adjacent high-rise buildings

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2020年第14期29卷 e1781-e1781页

作者： De Domenico, Dario Qiao, Haoshuai Wang, Qinhua Zhu, Zhiwen Marano, Giuseppe Univ Messina Dept Engn Messina Italy Shantou Univ Dept Civil & Environm Engn Shantou City Guangdong Peoples R China Key Lab Struct & Wind Tunnel Guangdong Higher Edu Shantou Peoples R China Politecn Torino Dept Struct Geotech & Bldg Engn Turin Italy

The tuned mass damper inerter (TMDI) is an enhanced variant of the tuned mass damper (TMD) that benefits from the mass-amplification effect of the inerter. Here, a multi-TMDI (MTMDI) system (comprising more than one TMDI) linking two adjacent high-rise buildings is presented as an unconventional seismic protection strategy. The relative acceleration response of the adjacent structures triggers large reaction forces of the inerter devices in the MTMDI, which in turn efficiently improve the seismic performance of the two buildings. By addressing a real project of two adjacent high-rise buildings connected by two corridors equipped with the proposed MTMDI system, the displacement-, interstory drift-, and acceleration-based parametric optimizations are separately performed by employing Nondominated Sorting Genetic algorithm II (NSGA-II) under 44 ground motions from the FEMA P695 far-field record set. It is found that the frequency content of the seismic input has strong impact on the MTMDI mitigation performance. Adopting realistic mass ratio constraints, the optimally designed MTMDI outperforms both conventional MTMD and single TMDI in acceleration control, while it is not much effective in mitigating the displacement response due to the highly flexible nature of the high-rise buildings, in contrast to other literature studies generally focused on low-to-medium rise buildings.

关键词： adjacent buildings coupled high-rise buildings inerter multiobjective evolutionary algorithm optimal design tuned mass damper

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High-efficiency scheme and optimisation technique for design of fragment-type isolation structure between multiple-input and multiple-output antennas

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IET MICROWAVES ANTENNAS & PROPAGATION 2015年第9期9卷 933-939页

作者： Ding, Dawei Wang, Gang Wang, Lu Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

Fragment-type structure has found its application in designing high isolation structure for multiple-input and multiple-output (MIMO) system. In this study, a novel design scheme and a high-efficiency optimisation searching technique for fragment-type isolation structure are proposed based on multiobjective evolutionary algorithm based on decomposition combined with enhanced genetic operators (MOEA/D-GO). Both considering non-uniform fragment cells in design space and combining two-dimensional median filtering operator in the original MOEA/D-GO can improve speed of optimisation searching while obtaining higher isolation and better envelope correlation coefficient at expense of capacity loss. With the high-efficiency optimisation search, more design objectives including the beam direction can be handled as well. The proposed design scheme and high-efficiency searching technique are demonstrated through design examples with two MIMO planar inverted-F antennas operating at 2.345-2.36 GHz and having a centre-to-centre interval of 15 mm. Comparison results among three designs verify the high effectiveness of the proposed scheme and optimisation searching technique.

关键词： MIMO communication planar inverted-F antennas UHF antennas evolutionary computation median filters antenna arrays optimisation technique high-efficiency scheme fragment-type isolation structure multiple-input and multiple-output antennas MIMO system design scheme optimisation searching technique multiobjective evolutionary algorithm enhanced genetic operators two-dimensional median filtering operator MOEA/D-GO optimisation searching MIMO planar inverted-F antennas optimisation searching technique frequency 2.345 GHz to 2.36 GHz

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Rapid multi-objective design optimisation of compact microwave couplers by means of physics-based surrogates

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IET MICROWAVES ANTENNAS & PROPAGATION 2016年第5期10卷 479-486页

作者： Koziel, Slawomir Bekasiewicz, Adrian Kurgan, Piotr Bandler, John W. Reykjavik Univ Sch Sci & Engn Engn Optimizat & Modeling Ctr Menntavegur 1 IS-101 Reykjavik Iceland Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

The authors introduce a methodology for fast multi-objective design optimisation of miniaturised microwave couplers. The approach exploits the surrogate-based optimisation paradigm with an underlying low-fidelity model constructed from an equivalent circuit of the structure under consideration, corrected through implicit and frequency space mapping. A fast prediction tool obtained this way is subsequently optimised by a multi-objective evolutionary algorithm to identify an initial approximation of the Pareto front, that is, a set of designs representing the best possible trade-offs between conflicting objectives. The correction/optimisation of the surrogate is then iterated by design space confinement and segmentation based on a Pareto set representation obtained thus far in the process. This aims at improving the surrogate model accuracy in the vicinity of the Pareto-optimal solutions. The technique is demonstrated by two design examples of compact rat-race couplers. Experimental validation is also provided.

关键词： evolutionary computation approximation theory microwave circuits passive networks rapid multiobjective design optimisation compact microwave couplers physics based surrogates miniaturised microwave couplers surrogate based optimisation paradigm equivalent circuit frequency space mapping multiobjective evolutionary algorithm initial approximation Pareto front design space confinement Pareto set representation compact rat-race couplers microwave passive circuits

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An effective evolutionary algorithm for the biobjective full truckload transportation service procurement problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 127卷 1012-1023页

作者： Zhang, Mo Hu, Qian Nanjing Univ Sch Management & Engn Nanjing 210093 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Sci Nanjing 210023 Jiangsu Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore

In truckload transportation service procurement, transit time is an important factor considered by shippers. In addition to transportation costs, transit time is also expected to be minimized so as to improve service quality and transportation efficiency. In this work, we study the full truckload transportation service procurement problem with transit time and develop a two-phase multiobjective evolutionary algorithm to explore the Pareto front of the problem. The algorithm encodes a chromosome of an individual using a set of winning carriers to reduce decision space. Each individual corresponds to a biobjective subproblem that has a set of solutions on its local Pareto front. The individuals are explored using fitness-based crossover and mutation operators in the first phase of the algorithm. The promising individuals that could produce nondominated solutions, and their local Pareto fronts are further explored by a sophisticated method in the second phase. Computational results show that the new fitness-based operators are effective and the solutions found by the algorithm can well approach the Pareto front.

关键词： multiobjective evolutionary algorithm A two-phase framework Full truckload transportation service procurement Transit time

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Enhanced fuzzy C-means clustering based cooperative spectrum sensing combined with multi-objective resource allocation approach for delay-aware CRNs

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IET COMMUNICATIONS 2020年第4期14卷 619-626页

作者： Sonti, Siva Reddy Prasad, Mutchakayala Siva Ganga KLEF Dept Elect & Commun Engn Vijayawada Andhra Pradesh India

In cognitive radio networks (CRNs), the resource allocation is viewed as a multi-objective optimisation problem in terms of limitation of quality of service, the capacity of a channel and the transmitted power. To overcome these individual issues many researchers have been undertaken, but it does not solve the multi-objective problems. In this study, the authors propose a multi-objective random walk grey wolf optimisation (MORWGWO) algorithm to enhance the resource allocation in the CRNs. Here, they combined the spectrum sensing process with the resource allocation process. Initially, an enhanced fuzzy C-means algorithm based cluster formation is proposed for spectrum sensing and then they model the resource allocation process and propose a MORWGWO algorithm for CRNs which generates the Pareto front inbetween of different objectives in a time-efficient manner. The simulation result shows that the proposed method significantly enhances the network performance in terms of delay, delivery ratio, throughput, network lifetime, energy consumption, and fairness index. Result shows that the proposed method has a better throughput of 23.22, 15.09, 6.13% for varying nodes and 27.25, 10.62, 7.09% for varying data transfer rates while comparing with the multiple objective particle swarm optimisation, non-dominated sorting genetic algorithm, and multi-objective evolutionary algorithm.

关键词： pattern clustering cognitive radio Pareto optimisation genetic algorithms particle swarm optimisation signal detection resource allocation radio spectrum management cooperative communication power consumption quality of service multiobjective optimisation problem spectrum sensing process MORWGWO algorithm multiple objective particle swarm optimisation nondominated sorting genetic algorithm multiobjective evolutionary algorithm multiobjective resource allocation cognitive radio networks delay-aware CRN multiobjective random walk grey wolf optimisation enhanced fuzzy C-means clustering ooperative spectrum sensing quality of service transmitted power Pareto front network lifetime energy consumption fairness index

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MEFES: An evolutionary proposal for the detection of exceptions in subgroup discovery. An application to Concentrating Photovoltaic Technology

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KNOWLEDGE-BASED SYSTEMS 2013年 54卷 73-85页

作者： Carmona, C. J. Gonzalez, P. Garcia-Domingo, B. del Jesus, M. J. Aguilera, J. Univ Jaen Dept Comp Sci SIMIDAT Res Grp Jaen 23071 Spain Univ Jaen Dept Elect & Automat Engn IDEA Res Grp Jaen 23071 Spain

Subgroup discovery is a broadly applicable data mining technique whose main objective is the search for descriptions of subgroups of data that are statistically unusual with respect to a property of interest. The obtaining of general rules describing as many instances as possible is preferred in subgroup discovery, but this can lead to less accurate descriptions that incorrectly describe some instances. Under certain conditions, these incorrectly-described instances can be grouped into exceptions. A new post-processing methodology for the detection of exceptions associated to previously discovered subgroups is presented in this paper. The purpose is to obtain a new description to improve the accuracy of the initial subgroup and to describe new small spaces in data with unusual behaviour within the subgroup. This post-processing methodology can be applied to the results of any subgroup discovery algorithm. A post-processing multiobjective evolutionary fuzzy system is developed following this methodology, the multiobjective evolutionary Fuzzy system for the detection of Exceptions in Subgroups (MEFES). A wide experimental study has been performed, supported by statistical tests, comparing the results obtained by representative subgroup discovery algorithms with those obtained after applying the post-processing algorithm. Finally, MEFES is applied in a real problem related to the description of the behaviour of a type of solar cell in the Concentrating Photovoltaic area providing useful information to the experts. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Subgroup discovery Exception rules multiobjective evolutionary algorithm evolutionary fuzzy system Concentrating Photovoltaic Technology

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