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A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

APPLIED SOFT COMPUTING 2014年第0期24卷 757-772页

作者： Lwin, Khin Qu, Rong Kendall, Graham Univ Nottingham Sch Comp Sci ASAP Grp Nottingham NG8 1BB England

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of- the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic algorithm ( NSGA-II), Strength Pareto evolutionary algorithm (SPEA-2), Pareto Envelope-based Selection algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： Optimization Procedure multi-objective evolutionary algorithm BRIEFCASE evolutionary algorithm

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Reservoir flood control operation using multi-objective evolutionary algorithm with decomposition and preferences

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APPLIED SOFT COMPUTING 2017年 50卷 21-33页

作者： Qi, Yutao Yu, Jusheng Li, Xiaodong Wei, Yanxi Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian Peoples R China RMIT Univ Sch Comp Sci & IT Melbourne Vic Australia

In this paper we propose a preference-based multi-objective optimization model for reservoir flood control operation (RFCO). This model takes the water preserving demand into consideration while optimizing two conflicting flood control objectives. A preference based multi-objective evolutionary algorithm with decomposition, named MOEA/D-PWA, is developed for solving the proposed RFCO model. For RFCO, it is challenging to define the preferred region formally, as the preference information is implicit and difficult to formulate. MOEA/D-PWA estimates the preferred region dynamically according to the final water level of solutions in the population, and then guides the search by propelling solutions towards the preferred region. Experimental results on four types of floods at the Ankang reservoir have illustrated that the suggested MOEA/D-PWA can successfully produce solutions in the preferred region of the Pareto front. The schedules obtained by MOEA/D-PWA can significantly reduce the flood peak and guarantee the dam safety as well. The proposed MOEA/D-PWA is also efficient in term of computational cost. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Reservoir flood control operation multi-objective evolutionary algorithm MOEA/D Preference

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Fuzzy rules for describing subgroups from Influenza A virus using a multi-objective evolutionary algorithm

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APPLIED SOFT COMPUTING 2013年第8期13卷 3439-3448页

作者： Carmona, C. J. Chrysostomou, C. Seker, H. del Jesus, M. J. Univ Jaen Dept Comp Sci Jaen 23071 Spain Univ Leicester Dept Genet Leicester LE1 7RH Leics England De Montfort Univ Sch Comp Ctr Computat Intelligent Leicester LE1 9BH Leics England

Extraction of biologically-meaningful knowledge is one of the important and challenging tasks in bioinformatics, in particular computational analysis of DNA and protein sequences, in order to identify biological function(s) and behaviour(s) of newly-extracted sequences. Computational intelligence techniques in corporation with sequence-driven features have been applied to tackle the problem and help classify different functional classes of the sequences. In order to study this problem, subgroup discovery algorithms together with a signal processing-based feature extraction method are applied, where the sequences are represented as a signal. The applicability of this method has been studied through four different Neuraminidase genes of Influenza A subtypes, H1N1, H2N2, H3N2 and H5N1. The results yielded not only higher predictive accuracy over these four classes of the proteins but also interpretable rule-based representation of the descriptive model with a significantly reduced feature set driven by means of the signal processing method. Subgroup discovery technique based on evolutionary fuzzy systems is expected to open new areas of research in bioinformatics and further help identify and understand more focused therapeutic protein targets. (C) 2013 Elsevier B. V. All rights reserved.

关键词： evolutionary fuzzy system Influenza A virus Neuraminidase Subgroup discovery multi-objective evolutionary algorithm

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Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm

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SOFT COMPUTING 2013年第9期17卷 1673-1686页

作者： Afsari, F. Eftekhari, M. Eslami, E. Woo, P. -Y. Shahid Bahonar Univ Kerman Dept Comp Engn Kerman Iran Shahid Bahonar Univ Kerman Dept Math Kerman Iran No Illinois Univ Dept Elect Engn De Kalb IL 60115 USA

The fuzzy decision tree (FDT) is a powerful, top-down, hierarchical search scheme to extract human interpretable classification rules. Furthermore, the FDT is considered an approach to model a system by making use of a descriptive language with fuzzy logic and fuzzy predicates. There exist two contradictory requirements with fuzzy modeling, namely, accuracy and interpretability. In this study, the subtractive clustering and a multi-objective evolutionary algorithm are used to develop a novel fuzzy modeling scheme based on the FDT classifier to construct an accurate and interpretable system that is defined as the interpretability-based fuzzy decision tree classifier. Two interpretability measures, namely, complexity interpretability and semantics interpretability are considered to reach acceptable accuracy. These measures are optimized as different objectives within the multi-objective framework. Results obtained in several benchmark classification problems are encouraging because they show the ability of the developed scheme while yielding accuracy comparable to that achieved by other methods like neural networks and the crisp decision tree (C4.5). The experimental results demonstrate the superiority of the developed scheme.

关键词： Fuzzy decision tree Interpretability measures Interpretability-based fuzzy decision tree classifier (IFDTC) multi-objective evolutionary algorithm

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Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第5期30卷 2257-2272页

作者： Fu, Yaping Wang, Hongfeng Tian, Guangdong Li, Zhiwu Hu, Hesuan Qingdao Univ Inst Complex Sci Qingdao 266071 Shandong Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Jilin Univ Coll Transportat Changchun 130022 Jilin Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Xidian Univ Sch Electromech Engn Xian 710071 Shaanxi Peoples R China

multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.

关键词： Flow shop scheduling Deteriorating scheduling multi-objective multi-agent scheduling multi-objective evolutionary algorithm multipopulation

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multi-objective Optimal Reactive Power Dispatch of Power Systems by Combining Classification-Based multi-objective evolutionary algorithm and Integrated Decision Making

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IEEE ACCESS 2020年 8卷 38198-38209页

作者： Zhang, Meng Li, Yang Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

For the purpose of addressing the multi-objective optimal reactive power dispatch (MORPD) problem, a two-step approach is proposed in this paper. First of all, to ensure the economy and security of the power system, the MORPD model aiming to minimize active power loss and voltage deviation is formulated. And then the two-step approach integrating decision-making into optimization is proposed to solve the model. Specifically speaking, the first step aims to seek the Pareto optimal solutions (POSs) with good distribution by using a multi-objective optimization (MOO) algorithm named classification and Pareto domination based multi-objective evolutionary algorithm (CPSMOEA). Furthermore, the reference Pareto-optimal front is generated to validate the Pareto front obtained using CPSMOEA;in the second step, integrated decision-making by combining fuzzy c-means algorithm (FCM) with grey relation projection method (GRP) aims to extract the best compromise solutions which reflect the preferences of decision-makers from the POSs. Based on the test results on the IEEE 30-bus and IEEE 118-bus test systems, it is demonstrated that the proposed approach not only manages to address the MORPD issue but also outperforms other commonly-used MOO algorithms including multi-objective particle swarm optimization (MOPSO), preference-inspired coevolutionary algorithm (PICEAg) and the third evolution step of generalized differential evolution (GDE3).

关键词： Optimization Decision making Reactive power Linear programming evolutionary computation Security Optimal reactive power dispatch multi-objective evolutionary algorithm integrated decision-making best compromise solution fuzzy c-means algorithm grey relation projection

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Discovering generalized design knowledge using a multi-objective evolutionary algorithm with generalization operators

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 143卷 1页

作者： Bang, Hyunseung Selva, Daniel Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA Texas A&M Univ Dept Aerosp Engn College Stn TX 77843 USA

The early-phase design of complex systems is a challenging task, as a decision maker has to take into account the intricate relationships among different design variables. A popular way to help decision makers easily identify important design features is to use data mining. However, many of the existing algorithms output design features that are too complex (e.g., conjunction of many literals with unrelated predicates), making it difficult for a user to understand, remember, and apply these features to find better designs. In this paper, we introduce a new data mining method that extracts compact design features through knowledge generalization. The proposed method performs a search over the space of features using a multi-objective evolutionary algorithm that contains a set of generalization operators in addition to conventional evolutionary operators. Both variables and feature types are generalized by using an ontology defining a set of domain-specific concepts and relationships. Generalization leads to more compact and insightful features, as generalized knowledge encompasses wider concepts. A comparative experiment is conducted on a real-world system architecting problem to demonstrate the gain in compactness of the extracted features without significant reductions in predictive power. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Design space exploration Knowledge discovery Feature extraction Data mining multi-objective evolutionary algorithm Adaptive operator selection

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Dynamic multi-objective evolutionary algorithm for IoT services

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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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Optimum plate-fin heat sinks by using a multi-objective evolutionary algorithm

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ENGINEERING OPTIMIZATION 2010年第4期42卷 305-323页

作者： Bureerat, S. Srisomporn, S. Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

This article demonstrates the practical applications of a multi-objective evolutionary algorithm (MOEA) namely population-based incremental learning (PBIL) for an automated shape optimization of plate-fin heat sinks. The computational procedure of multi-objective PBIL is detailed. The design problem is posed to find heat sink shapes which minimize the junction temperature and fan pumping power while meeting predefined constraints. Three sets of shape design variables used in this study are defined as: vertical straight fins with fin height variation, oblique straight fins with steady fin heights, and oblique straight fins with fin height variation. The optimum results obtained from using the various sets of design variables are illustrated and compared. It can be said that, with this sophisticated design system, efficient and effective design of plate-fin heat sinks is achievable and the best design variables set is the oblique straight fins with fin height variation.

关键词： heat sink optimum design multi-objective evolutionary algorithm finite volume method shape optimization computational fluid dynamics

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A hybrid multi-objective evolutionary algorithm with feedback mechanism

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APPLIED INTELLIGENCE 2018年第11期48卷 4149-4173页

作者： Lu, Chao Gao, Liang Li, Xinyu Zeng, Bing Zhou, Feng China Univ Geosci Sch Comp Sci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol 388 Lumo Rd Wuhan 430074 Hubei Peoples R China Army Engn Univ PLA Wuhan Wuhan 430075 Hubei Peoples R China

Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. multiple techniques include harmony search, genetic operator and differential evolution, which can improve the search diversity. Whereas hybrid selection mechanism contributes to the search efficiency by integrating the advantages of the static and adaptive selection scheme. Therefore, multiple techniques based on the hybrid selection strategy can effectively enhance the exploration ability of the MOHGD. Besides, we propose a feedback strategy to transfer some non-dominated solutions from the external archive to the parent population. This feedback strategy can strengthen convergence toward Pareto optimal solutions and improve the exploitation ability of the MOHGD. The proposed MOHGD has been evaluated on benchmarks against other state of the art MOEAs in terms of convergence, spread, coverage, and convergence speed. Computational results show that the proposed MOHGD is competitive or superior to other MOEAs considered in this paper.

关键词： Differential evolution Feedback mechanism Harmony search Hybrid selection mechanism multi-objective evolutionary algorithm

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