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Using health data repositories for developing clinical system software: a multi-objective fuzzy genetic approach

IET SOFTWARE 2020年第3期14卷 254-264页

作者： Raja, Bilal S. Asghar, Sohail COMSATS Univ Islamabad CS Dept Islamabad Pakistan

Evolution of technology has brought a revolution in various fields of sciences and amongst them, healthcare is one of the most critical and sensitive areas because of its connection with common masses' quality of life. The notion of integrating the healthcare system with the latest data repositories is to make disease prediction efficient, transparent, and reusable. Due to data heterogeneity, data repositories along with optimum classifiers help stakeholders to predict the disease more accurately without compromising the interpretability. evolutionary algorithms have shown great efficacy, accuracy, and interpretability in improving disease prediction for several datasets. However, the quest for the best classifier is still in evolution. In this research, a state-of-the-art medical data repository has been developed to give researchers of medical domain great ease of use in utilizing different datasets governed by a multi-objective evolutionary algorithm using fuzzy genetics. The proposed model called 'MEAF' is evaluated on various public repositories. A subset of these repositories includes breast cancer, heart, diabetes, liver, and hepatitis datasets. The results have been analyzed, which show competitive accuracy, sensitivity, and interpretability as compared to relevant research. A customised software application named 'MediHealth' is developed to supplement the proposed model that will facilitate the domain users.

关键词： fuzzy set theory data mining health care medical information systems genetic algorithms pattern classification decision support systems cancer decision making clinical system software multiobjective fuzzy genetic approach critical areas sensitive areas common masses healthcare system data repositories leading concerns optimal classifier wide variation evolutionary algorithms medical domain utilising different datasets multiobjective evolutionary algorithm fuzzy genetics public repositories hepatitis datasets medical decision-making process remote areas health facilities medical specialists health data repositories medical data repository customised software application MediHealth

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MOEA/D With Linear Programming for Double Row Layout Problem With Center-Islands

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第7期51卷 3549-3561页

作者： Zuo, Xingquan Liu, Xueqing Zhang, Qingfu Li, Weiping Wan, Xing Zhao, Xinchao Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China Beijing Baidu Netcom Sci Technol Co Ltd Dept Intelligent Vehicles Beijing 100085 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Peking Univ Sch Software & Microelect Beijing 100871 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China

Facility layout problems (FLPs) in hospitals are typically to arrange facilities or rooms along both sides of a corridor to minimize some objectives. In a hospital, very often there are center-islands to decrease the flow cost among facilities or rooms. However, these islands have not been considered before. In this article, we propose an FLP with center-islands that involves two parallel rows and center-islands. A mixed-integer program formulation is established for modeling it. A methodology for combining a multiobjective evolutionary algorithm based on decomposition (MOEA/D) and linear program is proposed to solve this problem. MOEA/D optimizes the sequence of facilities on two rows and center-islands while the linear program is embedded into MOEA/D to optimize the exact locations of center-islands. A tabu search with a local search is also integrated into MOEA/D to enhance its search capability. Experiments show that our proposed methodology can effectively solve the problem.

关键词： Layout Hospitals Linear programming evolutionary computation Search problems Computer science Telecommunications Double row layout problem (DRLP) linear programming (LP) multiobjective evolutionary algorithm tabu search

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Multi-objective generation dispatch considering the trade-off between economy and security

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第3期12卷 633-642页

作者： Qiu, Jing Zhao, Junhua Wang, Dongxiao Univ Sydney Sch Elect & Informat Engn Camperdown NSW 2006 Australia Chinese Univ Hong Kong Shenzhen 518100 Guangdong Peoples R China Univ Newcastle Ctr Intelligent Elect Networks Callaghan NSW 2308 Australia

This study presents a multi-objective generation dispatch model based on security-constrained optimal power flow, in order to handle the contradictory economic and security objectives. In addition to the deterministic N-1 security criterion, a risk index is proposed to quantify the system security degree on a probabilistic basis. The risk index is defined as the summation of the products of the contingency probability and the associated severity function. Moreover, the cost of interruptible load is added into the generation cost, formulating the economic objective. In the meantime, wind power outputs and load variations are considered as uncertainties in the dispatch process. Furthermore, a multi-objective evolutionary algorithm is employed and developed to provide a set of trade-off solutions. The proposed approach is verified by the 4-bus and IEEE 24-bus systems. Numerical results show that the proposed approach can effectively investigate the impacts of demand response on system economy and security. The obtained Pareto optimal solutions are less likely to incur insecurity risk and are robust to the studied stochastic variations.

关键词： load dispatching load flow power system security power system economics multiobjective generation dispatch security-constrained optimal power flow security objectives deterministic N-1 security criterion risk index multiobjective evolutionary algorithm IEEE 24-bus systems stochastic variations

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Geometrical design of plate-fin heat sinks using hybridization of MOEA and RSM

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IEEE TRANSACTIONS ON COMPONENTS AND PACKAGING TECHNOLOGIES 2008年第2期31卷 351-360页

作者： Srisompom, Sungkom Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

The work in this paper is aimed at demonstrating the practical multiobjective optimization of plate-fin heat sinks and the superiority of using a combined response surface method and multiobjective evolutionary optimizer over solely using the evolutionary optimizer. The design problem assigned is to minimize a heat sink junction temperature and fan pumping power. Design variables determine a heat sink geometry and inlet air velocity. Design constraints are given in such a way that the maximum and minimum fin heights are properly limited. Function evaluation is carried out by using finite volume analysis software. Two multiobjective evolutionary optimization strategies, real-code strength Pareto evolutionary algorithm with and without the use of a response surface technique, are implemented, to explore the Pareto optimal front. The optimum results obtained from both design approaches are compared and discussed. It is illustrated that the multiobjective evolutionary technique is a powerful tool for the multiobjective design of electronic air-cooled heat sinks. With the same design conditions and an equal number of function evaluations, the multiobjective optimizer in association with the response surface technique totally outperforms the other. The design parameters affecting the diversity of the Pareto front include fin thickness, fin height distribution, and inlet air velocity while the plate base thickness and the total number of fins of the non-dominated solutions tend to approach certain values.

关键词： finite volume method multiobjective evolutionary algorithm plate-fin heat sink response surface method strength Pareto evolutionary algorithm

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Multiple objective evolutionary algorithm for temporal linguistic rule extraction

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ISA TRANSACTIONS 2005年第2期44卷 315-327页

作者： DeLima, PG Yen, GG Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Autonomous temporal linguistic rule extraction is an application of growing interest for its relevance to both decision support systems and fuzzy controllers. In the presented work, rules are evaluated using three qualitative metrics based on their representation on the truth space diagram. Performance metrics are then treated as competing objectives and the multiple objective evolutionary algorithm is used to search for an optimal set of nondominant rules. Novel techniques for data pre-processing and rule set post-processing are designed that deal directly with the delays involved in dynamic systems. Data collected from a simulated hot and cold water mixer are used to validate the proposed procedure. (c) 2005 ISA-The Instrumentation, Systems, and Automation Society.

关键词： rule extraction knowledge discovery multiobjective evolutionary algorithm fuzzy logic

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Multi-objective optimisation technique for coupling matrix synthesis of lossy filters

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IET MICROWAVES ANTENNAS & PROPAGATION 2013年第11期7卷 926-933页

作者： Zhao, Yuting Wang, Gang Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Univ Sci & Technol China Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

Coupling matrix synthesis technique based on multi-objective evolutionary algorithm (MOEA) is proposed for pseudoelliptic low-pass filter prototypes with lossy resonators and arbitrary topology. MOEA-based synthesis can deal with multi-objective functions so that different filter characteristics of high-performance lossy filter, such as return loss, out of band rejection and passband flatness, can be optimised simultaneously. In the proposed MOEA-based lossy filter synthesis, a chromosome pair coding model is defined to treat real and imaginary parts of complex coupling matrix of a lossy filter, and additional constraints on Q-factor to characterise lossy resonator in the filter are applied. For demonstration, non-dominated sorting genetic algorithm II with extended arithmetic crossover and adaptive mutation operator are used to synthesise three symmetric/asymmetric lossy filters. The results indicate that the proposed MOEA-based synthesis technique can provide perfect passband ripple as required in high-performance microwave filters.

关键词： elliptic filters genetic algorithms low-pass filters microwave filters resonators multiobjective optimisation technique coupling matrix synthesis technique multiobjective evolutionary algorithm pseudoelliptic low-pass filter prototypes lossy resonators arbitrary topology multiobjective functions filter characteristics high-performance lossy filter return loss out-of-band rejection passband flatness MOEA-based lossy filter synthesis chromosome pair coding model nondominated sorting genetic algorithm II extended arithmetic crossover adaptive mutation operator symmetric-asymmetric lossy filters perfect passband ripple high-performance microwave filters

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Multi-objective transmission expansion planning in a smart grid using a decomposition-based evolutionary algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第16期10卷 4024-4031页

作者： Qiu, Jing Dong, Zhao Yang Meng, Ke Xu, Yan Zhao, Junhua Zheng, Yu Univ Newcastle Ctr Intelligent Elect Networks Newcastle NSW Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Chinese Univ Hong Kong Sch Elect Engn Shenzhen Peoples R China CSG Elect Power Res Inst Guangzhou Guangdong Peoples R China

The integration of large-scale renewable energy and demand response (DR) resources in smart grids have brought in emerging challenges for transmission expansion planning (TEP), particularly in terms of system security. The conventional TEP models have not fully addressed the cost and the feasibility of corrective control (CC) actions such as generation rescheduling and load curtailment under contingencies. Moreover, the optimality of CC depends on the pre-contingency state, the post-contingency state, as well as the existence and viability of the involved CC actions. In this study, first the authors have given the explicit definition of CC risk index (CCRI), which evaluates the expected system performance under a set of contingencies (i.e. risk of incurring security issues). With the authors' improvement, the CCRI is now mathematically tractable and may have wide applications to TEP problems. Afterwards, the authors have proposed a multi-objective TEP framework with tradeoffs between cost and risk. A relatively new yet superior multi-objective evolutionary algorithm called the multi-objective evolutionary algorithm (MOEA)/D is introduced and employed to find Pareto optimal solutions. The proposed model is numerically verified on the modified IEEE RTS 24-bus and 118-bus systems. According to the simulation results, the proposed model can provide information regarding variants of risks and coordinate the optimum planning and DR solutions.

关键词： power transmission planning smart power grids evolutionary computation Pareto optimisation multiobjective transmission expansion planning smart grid decomposition-based evolutionary algorithm large-scale renewable energy demand response resources DR resources TEP models corrective control actions generation rescheduling load curtailment post-contingency state pre-contingency state CC risk index CCRI multiobjective evolutionary algorithm MOEA-D Pareto optimal solutions modified IEEE RTS 24-bus systems 118-bus systems

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Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第5-8期64卷 915-932页

作者： Rahmati, Seyed Habib A. Zandieh, M. Yazdani, M. Islamic Azad Univ Qazvin Branch Young Researchers Club Qazvin Iran Shahid Beheshti Univ GC Dept Ind Management Management & Accounting Fac Tehran Iran Qazvin Islamic Azad Univ Dept Ind & Mech Engn Qazvin Iran

The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature's algorithm.

关键词： Scheduling Flexible job shop multiobjective evolutionary algorithm Non-dominated sorting genetic algorithm Non-dominated ranking genetic algorithm

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Multi-objective robust dynamic VAR planning in power transmission girds for improving short-term voltage stability under uncertainties

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第8期12卷 1929-1940页

作者： Han, Tong Chen, Yanbo Ma, Jin North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Modern power transmission grids are facing more and more critical short-term voltage stability problems. This study proposes a novel robust dynamic VAR planning approach for improving the short-term voltage stability level under uncertainties including the peak load level, the maximum proportion of dynamic load, fault clearing time and deviation of the actual capacity of dynamic VAR compensators from rated capacity when contingencies occur. The robust dynamic VAR planning problem is formulated as a multi-objective optimisation model with objectives including the investment cost, the expectation and robustness of the short-term voltage stability level. The complexity of the planning model is firstly reduced by selecting severe contingencies and potential buses, leading to a simplified multi-objective optimisation model. Latin hypercube sampling is then used for the uncertainty quantification. The simplified multi-objective optimisation model is then solved by the combination of a multi-objective evolutionary algorithm called NSGAII and extreme learning machine-based surrogate modelling with adaptive training data sampling. This combination significantly reduces the unaffordable computing burden. Simulations are carried on the IEEE 39-bus system, illustrating that the authors proposed methodology is reliable with high computational efficiency, and offering decision-makers planning solutions with high mean performance and strong robustness with respect to the short-term voltage stability level.

关键词： power system stability power transmission planning genetic algorithms power grids static VAr compensators power transmission faults sampling methods feedforward neural nets power engineering computing multiobjective robust dynamic VAR planning approach power transmission grids short-term voltage stability level critical short-term voltage stability problems peak load level dynamic load fault clearing time dynamic VAR compensators multiobjective optimisation model investment cost Latin hypercube sampling uncertainty quantification multiobjective evolutionary algorithm epsilon-NSGAII algorithm extreme learning machine-based surrogate modelling adaptive training data sampling IEEE 39-bus system

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Deep belief ensemble network based on MOEA/D for short-term load forecasting

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NONLINEAR DYNAMICS 2021年第3期105卷 2405-2430页

作者： Fan, Chaodong Ding, Changkun Xiao, Leyi Cheng, Fanyong Ai, Zhaoyang Hunan Univ Finance & Econ Sch Informat Technol & Management Changsha 410205 Hunan Peoples R China Anhui Polytech Univ Minist Educ Key Lab Adv Percept & Intelligent Control High En Wuhu 241000 Peoples R China Xiangtan Univ Foshan Green Intelligent Mfg Res Inst Foshan 528000 Peoples R China Xihua Univ Vehicle Measurement Control & Safety Key Lab Sich Chengdu 610039 Peoples R China Hunan Univ Coll Foreign Languages Inst Cognit Control & Biophys Linguist Changsha 410082 Hunan Peoples R China

While the demand for quality power supply is increasing, the traditional power grid cannot meet with the users' demand. Smart grid has become a new solution to quality power supply for its improved information sharing and intelligent power distribution. In the smart grid, the power load forecasting is the key to power distribution. So, this paper proposed a multiobjective deep belief ensemble network based on ensemble empirical mode decomposition (EEMD) to realize the high diversity and accuracy of the forecasting model. This method decomposes the data based on EEMD to reduce the complexity of the data and classifies the data with sample entropy, using a multiobjective evolutionary algorithm based on decomposition optimization algorithm to optimize network parameters with accuracy and diversity as objective functions. The comparison of this method with other intelligent prediction methods such as deep belief network (DBN) and deep belief network based on ensemble empirical mode decomposition (EEMD-DBN) on multiple datasets shows that this method can guarantee accuracy and has good generalization characteristics.

关键词： Ensemble empirical mode decomposition Sample entropy multiobjective evolutionary algorithm Deep belief network Ensemble learning

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