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Metrics to guide a multi-objective evolutionary algorithm for ordinal classification

NEUROCOMPUTING 2014年 135卷 21-31页

作者： Cruz-Ramirez, M. Hervas-Martinez, C. Sanchez-Monedero, J. Gutierrez, P. A. Univ Cordoba Dept Numer Anal & Comp Sci E-14071 Cordoba Spain

Ordinal classification or ordinal regression is a classification problem in which the labels have an ordered arrangement between them. Due to this order, alternative performance evaluation metrics are need to be used in order to consider the magnitude of errors. This paper presents a study of the use of a multi-objective optimization approach in the context of ordinal classification. We contribute a study of ordinal classification performance metrics, and propose a new performance metric, the maximum mean absolute error (MMAE). MMAE considers per-class distribution of patterns and the magnitude of the errors, both issues being crucial for ordinal regression problems. In addition, we empirically show that some of the performance metrics are competitive objectives, which justify the use of multi-objective optimization strategies. In our case, a multi-objective evolutionary algorithm optimizes an artificial neural network ordinal model with different pairs of metric combinations, and we conclude that the pair of the mean absolute error (MAE) and the proposed MMAE is the most favourable. A study of the relationship between the metrics of this proposal is performed, and the graphical representation in the two-dimensional space where the search of the evolutionary algorithm takes place is analysed. The results obtained show a good classification performance, opening new lines of research in the evaluation and model selection of ordinal classifiers. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Mean absolute error multi-objective evolutionary algorithm Ordinal measures Ordinal classification Ordinal regression Proportional odds model

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Dynamic index tracking via multi-objective evolutionary algorithm

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APPLIED SOFT COMPUTING 2013年第7期13卷 3392-3408页

作者： Chiam, S. C. Tan, K. C. Al Mamun, A. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

Index tracking has been gaining in popularity in recent years, as sustainable and stable yields exceeding market returns proved to be elusive. Leveraging on the search capability of evolutionary algorithm, this paper proposed a multi-objective evolutionary index tracking platform that could simultaneously optimize both tracking performance and transaction costs throughout the investment horizon and address various real-world implementation issues in index tracking. For model evaluation, a realistic instantiation of the index tracking optimization problem that accounted for stochastic capital injections, practical transactional cost structures and other real-world constraints was formulated. Portfolio rebalancing strategies for the alignment of the tracker portfolio to time-varying market conditions were investigated also. Empirical studies based on equity indices from major global markets were conducted and the results validated the tracking capability of the proposed index tracking system in out-of-sample data sets, whilst minimizing transaction costs throughout the investment horizon. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Index tracking Portfolio rebalancing multi-objective evolutionary algorithm

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Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization

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RENEWABLE ENERGY 2018年第Jan.期115卷 326-337页

作者： Biswas, Partha P. Suganthan, P. N. Amaratunga, Gehan A. J. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Cambridge Dept Engn Cambridge England

An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning facilities for erection, installation and future maintenance. This paper proposes an approach where several options of optimized usable windfarm layouts can be obtained in a single run of decomposition based multi-objective evolutionary algorithm (MOEA/D). A set of Pareto optimal vectors is obtained with objective as maximum output power at minimum wake loss i.e. at maximum efficiency. Maximization of both output power and windfarm efficiency are set as two objectives for optimization. The objectives thus formulated ensure that in any single Pareto optimal solution the number of turbines used are placed at most optimum locations in the windfarm to extract maximum power available in the wind. Case studies with actual manufacturer data for wind turbines of same as well as different hub heights and with realistic wind data are performed under the scope of this research study. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Wind turbine data Windfarm turbine placement Power output Efficiency multi-objective evolutionary algorithm Hub heights

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An improved multi-objective evolutionary algorithm for multiple-target asynchronous parallel selective disassembly sequence planning

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第10期237卷 1553-1569页

作者： Sun, Xiang Guo, Shunsheng Guo, Jun Du, Baigang Tang, Hongtao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China Hubei Digital Mfg Key Lab Wuhan Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn 122 Luoshi Rd Wuhan 430070 Peoples R China

The product's service life has been shortened rapidly along with the development of modern technologies and human esthetic evolutions, resulting in a great number of end-of-life (EOL) products. Disassembling renewable parts from EOL products for remanufacturing is of great significance to save resources and protect the environment. The existing studies on selective disassembly sequence planning (SDSP) largely focus on the sequential disassembly planning, which is inefficient especially for complex products because it is a linear process where only one component can be removed at a time. Thus, this work focuses on parallel SDSP and further proposes a multiple-target asynchronous parallel selective DSP (APSDSP) with the objectives of minimizing disassembly time and maximizing disassembly profit simultaneously. In APSDSP, operators can remove multiple components simultaneously as long as disassembly constraints are not violated, and without synchronization requirement between operators. The feasible disassembly sequence and disassembly direction sequence are generated by a space interference matrix method (SIMM) to meet the actual disassembly environment. Based on SIMM, an improved multi-objective evolutionary algorithm based on multiple neighborhood search strategy is developed to create the pareto frontier of the problem. Finally, two cases study are presented to validate the effectiveness of the proposed methodology. It gives enterprises new insights to reduce disassembly time and improve disassembly profit.

关键词： Selective disassembly sequencing planning asynchronous parallel disassembly multi-objective evolutionary algorithm pareto frontier remanufacturing

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A multi-objective evolutionary algorithm with Dynamic Topology and its Application to Network-Wide Flight Trajectory Planning

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2017年第1期10卷 1345-1354页

作者： Yan, Su Cai, Kaiquan Swaid, Majed Beihang Univ Sch Elect & Informat Engn Natl Key Lab CNS ATM Beijing 100191 Peoples R China Hamburg Univ Technol Inst Air Transportat Syst D-21079 Hamburg Germany

Although conventional multi-objective evolutionary optimization algorithms (MOEAs) are proven to be effective in general, they are less superior when applied to solve a large-scale combinational real-world optimization problem with tightly coupled decision variables. For the purpose to enhance the capability of MOEAs in such scenarios, one may consider the importance of interaction topology in information exchange among individuals of MOEAs. From this standpoint, this article proposes a non-dominated sorting genetic algorithm II with dynamic topology (DTNSGAII), which applies a dynamic individual interaction network topology to improve the crossover operation. The dynamic topology and inter-individual interaction are determined by the solution spread criterion in the objective space as well as the solution relationships and similarities in the decision space. The combination of two aspects contributes to the balance of the exploitation and exploration capability of the algorithm. Finally, as an example to real-world applications, the DTNSGAII is used to solve a network-wide flight trajectory planning problem, which demonstrates that the application of dynamic topology can improve the performance of the NSGA-II.

关键词： multi-objective evolutionary algorithm complex network flight trajectory planning

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Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第7期40卷 2421-2434页

作者： Yannibelli, Virginia Amandi, Analia UNCPBA Fac Cs Exactas ISISTAN Res Inst RA-7000 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn RA-1033 Buenos Aires DF Argentina

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective project scheduling multi-objective hybrid algorithm multi-objective simulated annealing algorithm multi-objective evolutionary algorithm Non-dominated solutions

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Quantitative Trait Loci Mapping Problem: An Extinction-Based multi-objective evolutionary algorithm Approach

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algorithmS 2013年第3期6卷 546-564页

作者： Ghaffarizadeh, Ahmadreza Eftekhari, Mehdi Esmailizadeh, Ali K. Flann, Nicholas S. Utah State Univ Dept Comp Sci Logan UT 84341 USA Shahid Bahonar Univ Kerman Dept Comp Engn Kerman *** Iran Shahid Bahonar Univ Kerman Dept Anim Sci Kerman *** Iran Inst Syst Biol Seattle WA 98109 USA Synthet Biomfg Inst Logan UT 84322 USA

The Quantitative Trait Loci (QTL) mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA) method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS) regression QTL models and considers the conflicting objectives of model simplicity versus model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.

关键词： QTL quantitative trait loci mapping multi-objective evolutionary algorithm partial least squares extinction-based EAs

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Mining High Quality Patterns Using multi-objective evolutionary algorithm

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3883-3898页

作者： Fang, Wei Zhang, Qiang Sun, Jun Wu, Xiaojun Jiangnan Univ Dept Comp Sci & Technol Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.

关键词： Data mining Itemsets Sociology Statistics Optimization evolutionary computation Pattern mining support utility occupancy multi-objective evolutionary algorithm

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Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning

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SWARM AND evolutionary COMPUTATION 2019年 44卷 480-495页

作者： Ramirez Atencia, Cristian Del Ser, Javier Camacho, David Univ Autonoma Madrid Dept Ingn Informat C Francisco Tomas & Valiente 11 E-28049 Madrid Spain TECNALIA OPTIMA Area E-48160 Zamudio Spain Univ Basque Country UPV EHU Dept Commun Engn E-48013 Bilbao Spain BCAM E-48009 Bilbao Spain

Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naive MOEA approach.

关键词： Unmanned air vehicle Mission planning Constraint satisfaction problem multi-objective evolutionary algorithm Weighted strategies

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Transfer Learning Based multi-objective evolutionary algorithm for Dynamic Workflow Scheduling in the Cloud

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2024年第4期12卷 1200-1217页

作者： Xie, Huamao Ding, Ding Zhao, Lihong Kang, Kaixuan Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing 100044 Peoples R China Minist Educ Key Lab Big Data & Artificial Intelligence Transpo Beijing 100044 Peoples R China

Managing scientific applications in the Cloud poses many challenges in terms of workflow scheduling, especially in handling multi-objective workflow scheduling under quality of service (QoS) constraints. However, most studies address the workflow scheduling problem on the premise of the unchanged environment, without considering the high dynamics of the Cloud. In this paper, we model the constrained workflow scheduling in a dynamic Cloud environment as a dynamic multi-objective optimization problem with preferences, and propose a transfer learning based multi-objective evolutionary algorithm (TL-MOEA) to tackle the workflow scheduling problem of dynamic nature. Specifically, an elite-led transfer learning strategy is proposed to explore effective parameter adaptation for the MOEA by transferring helpful knowledge from elite solutions in the past environment to accelerate the optimization process. In addition, a multi-space diversity learning strategy is developed to maintain the diversity of the population. To satisfy various QoS constraints of workflow scheduling, a preference-based selection strategy is further designed to enable promising solutions for each iteration. Extensive experiments on five well-known scientific workflows demonstrate that TL-MOEA can achieve highly competitive performance compared to several state-of-art algorithms, and can obtain triple win solutions with optimization objectives of minimizing makespan, cost and energy consumption for dynamic workflow scheduling with user-defined constraints.

关键词： Dynamic scheduling Cloud computing Processor scheduling Heuristic algorithms Quality of service Costs Optimization Dynamic workflow scheduling multi-objective evolutionary algorithm transfer learning quality of service constraints preference optimization

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