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Fuzzy multi-objective portfolio model based on semi-variance-semi-absolute deviation risk measures

SOFT COMPUTING 2019年第17期23卷 8159-8179页

作者： Yue, Wei Wang, Yuping Xuan, Hejun Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xian Polytech Univ Sch Sci Xian 710071 Shaanxi Peoples R China

The risk measure plays an important role for portfolio selection problem. The lower partial risk (downside risk) measures have been considered to be more in line with investor's attitude toward risk. The purpose of this paper is to construct a portfolio selection model in the lower partial risk framework. First, semi-variance and semi-absolute deviation risk measures are used as double-risk measures simultaneously, which can overcome the shortcomings of both semi-variance risk measure and semi-absolute deviation risk measure and can provide additional strengths and flexibility. Second, to address a real portfolio selection problem, by considering the transaction cost and liquidity of portfolio, and regarding the returns of risk assets as LR-type fuzzy variables, we present a fuzzy multi-objective portfolio selection model. Third, in order to limit the proportion of the portfolio invested in assets with common characteristics and to avoid very small holdings, the quantity and class constraints are introduced into the model. Finally, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Furthermore, some experiments are conducted by using the data of Shanghai Stock Exchange, and the results indicate the efficiency and effectiveness of the proposed model and algorithm.

关键词： Portfolio selection The lower partial risk measures LR-fuzzy variable Possibilistic moments multi-objective evolutionary algorithm

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A multi-objective hot-rolling scheduling problem in the compact strip production

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APPLIED MATHEMATICAL MODELLING 2019年 73卷 327-348页

作者： Pan, Quan-Ke Gao, Liang Wang, Ling Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Tsinghua Univ TNList Dept Automat Beijing 100084 Peoples R China

This paper addresses a hot-rolling scheduling problem from compact strip production processes. At first, a mathematical model that consists of two coupled sub-problems is presented. The first sub-problem is the sheet strip assignment problem that is about how to assign sheet-strips to rolling-turns with the objective of minimizing virtual sheet-strips. The second is the sheet-strip sequencing problem that is about how to sort the sheet-strips in each rolling-turn with the objective of minimizing the maximal changes in thickness between adjacent sheet-strips and the change times of the thickness so as to ensure high quality sheet-strips to be produced. And then, an improved hot-rolling scheduling heuristic is proposed to solve the sheet-strip assignment problem. A multi-objective evolutionary algorithm is developed to find the Pareto optimal or near-optimal solutions for the sheet-strip sequencing problem. Besides, the problem-specific knowledge is explored. The key operators including crossover operator, mutation operator and repair operator are designed for the multi-objective evolutionary algorithm. At last, extensive experiments based on real-world instances from a compact strip production process are carried out. The results demonstrate the effectiveness of the proposed algorithms for solving the hot-rolling scheduling problem under consideration. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Scheduling Heuristic multi-objective evolutionary algorithm Hot-rolling Compact strip production

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A multi-objective Community Detection algorithm for Directed Network Based on Random Walk

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IEEE ACCESS 2019年 7卷 162652-162663页

作者： Wen, Xuyun Lin, Ying Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Sun Yat Sen Univ Dept Psychol Guangzhou 510006 Peoples R China

Uncovering community structure is an important technique for studying complex networks. While a large bulk of algorithms have been proposed for community detection in recent years, most of them were designed for undirected networks. Considering many real-world networks are by nature directed, it is necessary to develop community detection methods that can handle directed networks. In this work, we formulates a multi-objective framework for community detection in directed networks and proposes a multi-objective evolutionary algorithm for finding efficient solutions under this framework. Specifically, based on the theory that an efficient partition of directed networks should have larger network information flow within the community than that between different communities, we first designed two conflicting objective functions based on PageRank random walk, one of which is to maximize within-community transition probability, and the other is to minimize between-community transition probability. By optimizing these two objectives simultaneously, we modelled the problem of community detection as a multi-objective optimization problem, and then developed a novel multi-objective evolutionary algorithm to solve it. Particularly, to guarantee the capability of searching the optimal solution, our proposed method designed/adopted the directed-network-specific population initialization method and evolutionary operator by introducing label propagation algorithm into multi-objective genetic algorithm. Comparison with other four art-of-the-state algorithms, our method showed the competitive performance on both synthetic and real-world networks. Moreover, attributing to the multi-objective framework, the proposed method could generate multiple optimal network partitions in a single run, which provides a hierarchical description of community structure of the network.

关键词： Community detection directed network multi-objective evolutionary algorithm PageRank random walk

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Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第6期233卷 1391-1402页

作者： Ma, He Li, Ziyang Tayarani, Mohamad Lu, Guoxiang Xu, Hongming Yao, Xin Univ Birmingham Sch Engn Birmingham B15 2TT W Midlands England Univ Birmingham Sch Comp Sci Birmingham W Midlands England

For modern engines, the number of adjustable variables is increasing considerably. With an increase in the number of degrees of freedom and the consequent increase in the complexity of the calibration process, traditional design of experiments-based engine calibration methods are reaching their limits. As a result, an automated engine calibration approach is desired. In this paper, a model-based computational intelligence multi-objective optimization approach for gasoline direct injection engine calibration is developed, which can optimize the engine's indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number simultaneously, by intelligently adjusting the engine actuators' settings through Strength Pareto evolutionary algorithm 2. A mean-value model of gasoline direct injection engine is developed in the author's earlier work and used to predict the performance of indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number with given value of intake valves opening timing, exhaust valves closing timing, spark timing, injection timing, and rail pressure. Then a co-simulation platform is established for the introduced intelligence engine calibration approach in the given engine operating condition. The co-simulation study and experimental validation results suggest that the developed intelligence calibration approach can find the optimal gasoline direct injection engine actuators' settings with acceptable accuracy in much less time, compared to the traditional approach.

关键词： Computational intelligence multi-objective evolutionary algorithm gasoline direct injection engine calibration

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A posterior preference articulation approach to Kansei engineering system for product form design

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RESEARCH IN ENGINEERING DESIGN 2019年第1期30卷 3-19页

作者： Li, Yongfeng Shieh, Meng-Dar Yang, Chih-Chieh Jiangsu Normal Univ Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Natl Cheng Kung Univ Dept Ind Design 1 Univ Rd Tainan 70101 Taiwan Southern Taiwan Univ Sci & Technol Dept Multimedia & Entertainment Sci 1 Nantai St Tainan 71005 Taiwan

Understanding the needs of consumers is essential to the success of product design. Affective responses are a reflection of affective needs, often encompassing many aspects. Therefore, the process of designing products capable of satisfying multiple affective responses (MARs) falls into the category of multi-objective optimization (MOO). To solve the MOO problem, most existing approaches require the information for decision-making before or during the solving process, which limits their usefulness to designers or consumers. This paper proposes a posterior preference articulation approach to Kansei engineering system aimed at optimizing product form design to deal with MARs simultaneously. Design analysis is first used to identify design variables and MARs. Based on these results, a MOO model that involves maximizing MRAs is constructed. An improved version of the strength Pareto evolutionary algorithm (SPEA2) is applied to solve this MOO model so as to obtain Pareto solutions. After that, the Choquet fuzzy integral, which has the ability to take into account the interaction among the MARs, is employed to determine the optimal design from the Pareto solutions in accordance with the consumer preference. A case study involving the design of a vase form was conducted to illustrate the proposed approach. The results demonstrate that this approach can effectively obtain the optimal design solution, and be used as a universal approach for optimizing product form design concerning MARs.

关键词： Product form design Kansei engineering Decision making Posterior preference articulation approach multi-objective evolutionary algorithm

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A multi-objective Design of In-Building Distributed Antenna System Using evolutionary algorithms 1

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39th SGAI International Conference on Techniques and Applications of Artificial Intelligence (AI)

作者： AlShanqiti, Khawla Poon, Kin Shakya, Siddhartha Sleptchenko, Andrei Ouali, Anis Khalifa Univ Emirates ICT Innovat Ctr EBTIC Abu Dhabi U Arab Emirates Khalifa Univ Dept Ind & Syst Engn Abu Dhabi U Arab Emirates

ISBN: (数字)9783030348854

ISBN: (纸本)9783030348854;9783030348847

The increasing data traffic inside buildings requires maintaining good cellular network coverage for indoor mobile users. Passive In-building Distributed Antenna System (IB-DAS) is one of the most efficient methods to provide an indoor solution that meets the signal strength requirements. It is a network of spatially distributed antennas in a building connected to telephone rooms which are then connected to a Base Transmission Station (BTS). These connections are established through passive coaxial cables and splitters. The design of IB-DAS is considered to be challenging due to the power-sharing property resulting in two contradicting objectives: minimizing the power usage at the BTS (long-term cost) and minimizing the design components cost (short-term cost). Different attempts have been made in the literature to solve this problem. Some of them are either lacking the consideration of all necessary aspects or facing scalability issues. Additionally, most of these attempts translate the IB-DAS design into a mono-objective problem, which leads to a challenging task of determining a correct combined objective function with justified weighting factors associated with each objective. Moreover, these approaches do not produce multiple design choices which may not be satisfactory in practical scenarios. In this paper, we propose a multi-objective algorithm for designing IB-DAS. The experimental results show the success of this algorithm to achieve our industrial partner's requirement of providing different design options that cannot be achieved using mono-objective approaches.

关键词： Distributed Antenna System multi-objective evolutionary algorithm Non-dominated Sorting Genetic algorithm Crowding distance Normalization

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Gene Subset Selection for Leukemia Classification Using Microarray Data

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CURRENT BIOINFORMATICS 2019年第4期14卷 353-358页

作者： Fajila, Mohamed Nisper Fathima South Eastern Univ Sri Lanka Fac Appl Sci Dept Math Sci Sammanthurai Sri Lanka

Background: Cancer subtype identification is an active research field which helps in the diagnosis of various cancers with proper treatments. Leukemia is one such cancer with various subtypes. High throughput technologies such as Deoxyribo Nucleic Acid (DNA) microarray are highly active in the field of cancer detection and classification alternatively. objective: Yet, a precise analysis is important in microarray data applications as microarray experiments provide huge amount of data. Gene selection techniques promote microarray usage in the field of medicine. The objective of gene selection is to select a small subset of genes, which are the most informative in classification. Method: In this study, multi-objective evolutionary algorithm is used for gene subset selection in Leukemia classification. An initial redundant and irrelevant gene removal is followed by multi-objective evolutionary based gene subset selection. Gene subset selection highly influences the perfect classification. Thus, selecting the appropriate algorithm for subset selection is important. Results: The performance of the proposed method is compared against the standard genetic algorithm and evolutionary algorithm. Three Leukemia microarray datasets were used to evaluate the performance of the proposed method. Perfect classification was achieved for all the datasets only with few significant genes using the proposed approach. Conclusion: Thus, it is obvious that the proposed study perfectly classifies Leukemia with only few significant genes.

关键词： Classification gene selection microarray multi-objective evolutionary algorithm redundant and irrelevant gene significant genes

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multi-objective resource allocation for Edge Cloud based robotic workflow in smart factory

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 97卷 119-130页

作者： Afrin, Mahbuba Jin, Jiong Rahman, Ashfaqur Tian, Yu-Chu Kulkarni, Ambarish Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia CSIRO Data61 Sandy Bay Tas 7005 Australia Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld 4001 Australia Swinburne Univ Technol Sch Engn Melbourne Vic 3122 Australia

multi-robotic services are widely used to enhance the efficiency of Industry 4.0 applications including emergency management in smart factory. The workflow of these robotic services consists of data hungry, delay sensitive and compute intensive tasks. Generally, robots are not enriched in computational power and storage capabilities. It is thus beneficial to leverage the available Cloud resources to complement robots for executing robotic workflows. When multiple robots and Cloud instances work in a collaborative manner, optimal resource allocation for the tasks of a robotic workflow becomes a challenging problem. The diverse energy consumption rate of both robot and Cloud instances, and the cost of executing robotic workflow in such a distributed manner further intensify the resource allocation problem. Since the tasks are inter-dependent, inconvenience in data exchange between local robots and remote Cloud also degrade the service quality. Therefore, in this paper, we address simultaneous optimization of makespan, energy consumption and cost while allocating resources for the tasks of a robotic workflow. As a use case, we consider resource allocation for the robotic workflow of emergency management service in smart factory. We design an Edge Cloud based multi-robot system to overcome the limitations of remote Cloud based system in exchanging delay sensitive data. The resource allocation for robotic workflow is modelled as a constrained multi-objective optimization problem and it is solved through a multi-objective evolutionary approach, namely, NSGA-II algorithm. We have redesigned the NSGA-II algorithm by defining a new chromosome structure, pre-sorted initial population and mutation operator. It is further augmented by selecting the minimum distant solution from the non-dominated front to the origin while crossing over the chromosomes. The experimental results based on synthetic workload demonstrate that our augmented NSGA-II algorithm outperforms the state-of-t

关键词： Resource allocation multi-robot system Edge cloud Workflow management Smart factory multi-objective evolutionary algorithm

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Energy and spectral efficiency tradeoff in massive MIMO systems with multi-objective adaptive genetic algorithm

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SOFT COMPUTING 2019年第16期23卷 7163-7179页

作者： Hei, Yongqiang Zhang, Cong Song, Weiting Kou, Yanchun Xidian Univ State Key Lab ISN Xian Shaanxi Peoples R China

Spectral efficiency (SE) and energy efficiency (EE) are both important metrics in massive multiple-input multiple-output (MIMO) systems. However, maximizing EE and SE is always conflicting with each other, and they can hardly be achieved simultaneously. In this paper, we focus on the tradeoff optimization between EE and SE in multiuser massive MIMO systems in terms of the number of transmit antennas and the transmit power. Different from the previous EE-oriented or SE-oriented method, the EE-SE tradeoff problem is formulated into a multi-objective optimization problem. To efficiently attain the Pareto optimal front (POF) of EE-SE tradeoff, a multi-objective adaptive genetic algorithm, inspired by the non-dominated sorting genetic algorithm (NSGA-II), is proposed to improve the convergence speed. Experimental comparisons against several well-known multi-objective algorithms show that the proposed algorithm can quickly adapt to the true POF of EE-SE tradeoff and maintain good performance on benchmark functions in terms of the adopted performance metrics.

关键词： EE-SE tradeoff Massive MIMO multi-objective evolutionary algorithm

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multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions

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evolutionary COMPUTATION 2019年第2期27卷 291-311页

作者： Ceberio, Josu Calvo, Borja Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Donostia San Sebastian 20018 Spain BCAM Bilbao 48009 Spain

In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to use multi-objective algorithms in their optimisation. In this article, we follow up this idea by presenting a methodology for multi-objectivising combinatorial optimisation problems based on elementary landscape decompositions of their objective function. Under this framework, each of the elementary landscapes obtained from the decomposition is considered as an independent objective function to optimise. In order to illustrate this general methodology, we consider four problems from different domains: the quadratic assignment problem and the linear ordering problem (permutation domain), the 0-1 unconstrained quadratic optimisation problem (binary domain), and the frequency assignment problem (integer domain). We implemented two widely known multi-objective algorithms, NSGA-II and SPEA2, and compared their performance with that of a single-objective GA. The experiments conducted on a large benchmark of instances of the four problems show that the multi-objective algorithms clearly outperform the single-objective approaches. Furthermore, a discussion on the results suggests that the multi-objective space generated by this decomposition enhances the exploration ability, thus permitting NSGA-II and SPEA2 to obtain better results in the majority of the tested instances.

关键词： multi-objectivisation combinatorial optimisation elementary landscape decomposition multi-objective evolutionary algorithm

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