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multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 486卷 1-24页

作者： Shang, Ronghua Liu, Huan Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

This paper proposes a relatively all-purpose network clustering technique based on the framework of multi-objective evolutionary algorithms, which can effectively dispose the issue of community detection in unsigned social networks, as well as in signed social networks. Firstly, we formulate a generalized similarity function to construct a similarity matrix, and then a pre-partitioning strategy is projected according to the similarity matrix. The pre-partitioning strategy merely considers nodes with high similarity values, which avoids the interference of noise nodes during the label update phase. In this way, at the initial phase of the algorithm, nodes with strong connections are fleetly gathered into sub communities. Secondly, we elaborately devise a crossover operator, called cross-merging operator, to merge sub-communities generated by the pre-partitioning technique. Moreover, a special mutation operator, based on the similarity matrix of nodes, is implemented to adjust boundary nodes connecting different communities. Finally, to handle different types of networks, we, therefore, have presented the novel multi-objective optimization models for this issue. Through a bulk of rigorous experiments on both unsigned and signed social networks, the preeminent clustering performance illustrate that the proposed algorithm is capable of mining communities effectively. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Community detection multi-objective evolutionary algorithm Similarity matrix Pre-partitioning strategy Social network

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Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

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SCIENTIFIC REPORTS 2024年第1期14卷 1-27页

作者： Hashemi, Syed Mohsen Samare Robati, Amir Kazerooni, Mohammad Ali Islamic Azad Univ Dept Civil Engn Kerman Branch Kerman Iran

The optimal operation of the multi-purpose reservoir system is a difficult, and, sometimes, non-linear problem in multi-objective optimization. By simulating biological behavior, meta-heuristic algorithms scan the decision space and can offer a set of points as a group of solutions to a problem. Because it is essential to simultaneously optimize several competing objectives and consider relevant constraints as the main problem in many optimization problems, researchers have improved their ability to solve multi-objective problems by developing complementary multi-objective algorithms. Because the AHA algorithm is new, its multi-objective version, MOAHA (multi-objective artificial hummingbird algorithm), was used in this study and compared with two novel multi-objective algorithms, MOMSA and MOMGA. Schaffer and MMF1 were used as two standard multi-objective benchmark functions to gauge the effectiveness of the proposed method. Then, for 180 months, the best way to operate the reservoir system of the Karun River basin, which includes Karun 4, Karun 3, Karun 1, Masjed-e-Soleyman, and Gotvand Olia dams to generate hydropower energy, supply downstream demands (drinking, agriculture, industry, environmental), and control flooding was examined from September 2000 to August 2015. Four performance appraisal criteria (GD, S, Delta, and MS) and four evaluation indices (reliability, resiliency, vulnerability, and sustainability) were used in Karun's multi-objective multi-reservoir problem to evaluate the performance of the multi-objective algorithm. All three algorithms demonstrated strong capability in criterion problems by using multi-objective algorithms' criteria and performance indicators. The large-scale (1800 dimensions) of the multi-objective operation of the Karun Basin reservoir system was another problem. With a minimum of 1441.71 objectives and an average annual hydropower energy manufacturing of 17,166.47 GW, the MOAHA algorithm demonstrated considerable ability co

关键词： multi-objective evolutionary algorithm multi-reservoir systems Karun basin

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Causality on longitudinal data: Stable specification search in constrained structural equation modeling

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STATISTICAL METHODS IN MEDICAL RESEARCH 2018年第12期27卷 3814-3834页

作者： Rahmadi, Ridho Groot, Perry van Rijn, Marieke H. C. van den Brand, Jan A. J. G. Heins, Marianne Knoop, Hans Heskes, Tom Univ Islam Indonesia Dept Informat Sleman Indonesia Radboud Univ Nijmegen Inst Comp & Informat Sci Nijmegen Netherlands Radboud Univ Nijmegen Med Ctr Dept Nephrol Nijmegen Netherlands Netherlands Inst Hlth Serv Res Utrecht Netherlands Univ Amsterdam Acad Med Ctr Dept Med Psychol Amsterdam Netherlands

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

关键词： Longitudinal data causal modeling structural equation model stability selection multi-objective evolutionary algorithm chronic fatigue syndrome chronic kidney disease Alzheimer's disease

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Nadir point estimation for many-objective optimization problems based on emphasized critical regions

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SOFT COMPUTING 2017年第9期21卷 2283-2295页

作者： Wang, Handing He, Shan Yao, Xin Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist Educ Xian 710071 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Nadir points play an important role in many-objective optimization problems, which describe the ranges of their Pareto fronts. Using nadir points as references, decision makers may obtain their preference information for many-objective optimization problems. As the number of objectives increases, nadir point estimation becomes a more difficult task. In this paper, we propose a novel nadir point estimation method based on emphasized critical regions for many-objective optimization problems. It maintains the non-dominated solutions near extreme points and critical regions after an individual number assignment to different critical regions. Furthermore, it eliminates similar individuals by a novel self-adaptive -clearing strategy. Our approach has been shown to perform better on many-objective optimization problems (between 10 objectives and 35 objectives) than two other state-of-the-art nadir point estimation approaches.

关键词： multi-objective evolutionary algorithm Nadir point Critical region Many-objective optimization problem Decision making

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A fast nondominated sorting-based MOEA with convergence and diversity adjusted adaptively

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JOURNAL OF SUPERCOMPUTING 2024年第2期80卷 1426-1463页

作者： Gao, Xiaoxin He, Fazhi Zhang, Songwei Luo, Jinkun Fan, Bo Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Zhengzhou Inst Elect & Mech Engn Zhengzhou 450015 Peoples R China Wuhan Univ Inst Sci & Technol Dev Wuhan 430072 Peoples R China

In the past few decades, to solve the multi-objective optimization problems, many multi-objective evolutionary algorithms (MOEAs) have been proposed. However, MOEAs have a common difficulty: because the diversity and convergence of solutions are often two conflicting conditions, the balance between the diversity and convergence directly determines the quality of the solutions obtained by the algorithms. Meanwhile, the nondominated sorting method is a costly operation in part Pareto-based MOEAs and needs to be optimized. In this article, we propose a multi-objective evolutionary algorithm framework with convergence and diversity adjusted adaptively. Our contribution is mainly reflected in the following aspects: firstly, we propose a nondominated sorting-based MOEA framework with convergence and diversity adjusted adaptively;secondly, we propose a novel fast nondominated sorting algorithm;thirdly, we propose a convergence improvement strategy and a diversity improvement strategy. In the experiments, we compare our method with several popular MOEAs based on two widely used performance indicators in several multi-objective problem test instances, and the empirical results manifest the proposed method performs the best on most test instances, which further demonstrates that it outperforms all the comparison algorithms.

关键词： Nondominated sorting multi-objective evolutionary algorithm Convergence Diversity Adjusted adaptively

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Integrated Mechanical and Thermodynamic Optimization of an Engine Linkage Using a multi-objective Genetic algorithm

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JOURNAL OF MECHANICAL DESIGN 2015年第2期137卷 1-4页

作者： Sullivan, Thomas A. van de Ven, James D. Northrop, William F. McCabe, Kieran Univ Minnesota Dept Mech Engn Minneapolis MN 55455 USA

In order to improve the thermodynamic efficiency of an internal combustion engine (ICE), a Stephenson-III six-bar linkage is optimized to serve as a replacement for the traditional slider-crank. Novel techniques are presented for formulating the design variables in the kinematic optimization that guarantee satisfaction of the Grashof condition and of transmission angle requirements without the need for an explicit constraint function. Additionally, a nested generalization of the popular NSGA-II algorithm is presented that allows simultaneous optimization of the kinematic, dynamic, and thermodynamic properties of the mechanism. This approach successfully solves the complex six-objective optimization problem, with challenges for future refinement including improvement of the combustion simulation to attain better accuracy without prohibitive computational expense.

关键词： thermodynamic optimization Thermodynamic properties multi-objective evolutionary algorithm thermal efficiency Engines design variable kinematics Genetic algorithm (GA)

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An effective method for solving multiple travelling salesman problem based on NSGA-II

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SYSTEMS SCIENCE & CONTROL ENGINEERING 2019年第2期7卷 121-129页

作者： Yang, Shuai Shao Yunfeng Zhang Kai Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan Hubei Peoples R China

In this paper, an effective multi-objective evolutionary algorithm is proposed to solve the multiple travelling salesman problem. In order to obtain minimum total visited distance and minimum range between all salesmen, some novel representation, crossover and mutation operators are designed to enhance the local and global search behaviours, then NSGA-II framework is applied to find well-convergent and well-diversity non-dominated solutions. The proposed algorithm is compared with several state-of-the-art approaches, and the comparison results show the proposed algorithm is effective and efficient to solve the multiple travelling salesman problems.

关键词： multi-objective evolutionary algorithm multiple travelling salesman problem NSGA-II

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A heuristic non-dominated sorting genetic algorithm-II for satellite-module layout optimisation

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2010年第1-3期38卷 228-235页

作者： Xu, Y. Li, C. Shi, Y. J. Teng, H. F. Dalian Univ Technol Dept Comp Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Dept Mech Engn Dalian 116024 Peoples R China

This paper proposes a Heuristic Non-dominated Sorting Genetic algorithm-II (HNSGA-II) to solve a layout optimisation problem such as that of satellite-modules. Firstly, we apply a diversity degree method to evaluate the dominance (or inferiority) of solutions in the same front instead of the crowding-distance method. Secondly, a filter technique of a Pareto solution set is introduced to preserve the elite individuals more effectively. Thirdly, three kinds of operators are adopted to improve search performance. Finally, we give two heuristic layout strategies in the optimisation process. The numerical experiments of a simplified satellite-module layout design show the feasibility and the effectiveness of the proposed algorithm.

关键词： layout optimisation satellite-module multi-objective evolutionary algorithm NSGA-II Pareto solution heuristic non-dominated sorting

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multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

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SN APPLIED SCIENCES 2019年第9期1卷 1072页

作者： Tey, Jing Yuen Shak, Katrina Pui Yee Univ Tunku Abdul Rahman Jalan Sungai Long Kajang 43000 Selangor Malaysia

Formula 1 is considered an important motorsport event that has driven the development of cutting-edge automotive technology. Similarly, virtual formula competition is introduced at the university level to harness technological advances for automotive development among universities. Performance and fuel efficiency have always been two conflicting objectives in determining the optimal powertrain design. In this paper, state-of-the-art multi-objective evolutionary algorithms (MOEAs) are employed to optimize the vehicle powertrain design based on competing event and the on-track performance through software in-the-loop optimization approach. Unlike conventional powertrain optimization approach where the powertrain design is optimized again standard urban driving profiles to achieve optimum driving performance and fuel consumption which is not suitable to be employed to optimize race car powertrain design. The powertrain design is optimized and benchmarked in multiple dynamic test events i.e. Acceleration, Autocross and Endurance. Each event measures different performance parameters (i.e. lap time and fuel consumption) A total of nine design parameters are optimized in this study. Three design parameters (employed to formulate the progressive gear calculation of six gear ratios), five gear shifting rpm (revolutions per minute) and an aerodynamic downforce scale value (for front and rear downforce). Two stages of optimization solution are proposed to handle such complicated design requirement. NSGA-II was identified as the most efficient algorithms among the MOEAs employed in this study;especially in terms of computational time, Pareto distribution and Pareto design. Overall, the optimized powertrain design showed significant improvement in all events i.e. energy efficiency was increased by 13.7% for the endurance event while lap time was reduced by 0.7 and 1.2% in the acceleration and autocross events, respectively.

关键词： Powertrain design multi-objective evolutionary algorithm Vehicle performance Fuel efficiency

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Optimising pump system with constant and variable speed pumps: case study

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2011年第4期12卷 412-420页

作者： Fang, Haien Wu, Wenyan Lv, Mou Gao, Jinliang Harbin Inst Technol Sch Municipal & Environm Engn POB 2624 Harbin 150090 Heilongjiang Peoples R China Staffordshire Univ Fac Comp Engn & Technol Beaconside Stafford ST18 0DF England Qingdao Technol Univ Sch Civil & Environm Engn Qingdao 266033 Peoples R China

Optimising pump scheduling is a complex problem, which involves a large space search, continuous and discrete variables, physical and operational constraints and also multi-objectives. In this paper, multi-objective evolutionary algorithms (MOEAs) combined with a repair mechanism are used to solve the optimal operation problem within water supply system. In this work two objectives are minimised: operation cost (energy cost + treatment cost) and maintenance cost, while one objective is maximised: service level of hydraulic. Decision variables are the settings of the pumps and speed ratio of variable-speed pumps at a time step of the total operational time horizon. A mixed coding methodology and a new crossover operator are developed according to the characteristics of decision variables. Three well-known MOEAS (NSGA-II, epsilon-MOEA and SPEA2) are implemented and compared. Practical application of this method shows that it can make efficient decision to support the operators.

关键词： water supply network pump system multi-objective evolutionary algorithm MOEA mixed coding repair mechanism

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