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An improved MOEA/D algorithm for bi-objective optimization problems with complex Pareto fronts and its application to structural optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 92卷 430-446页

作者： Ho-Huu, V. Hartjes, S. Visser, H. G. Curran, R. Delft Univ Technol Fac Aerosp Engn Delft Netherlands

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been recognized as a promising method for solving multi-objective optimization problems (MOPs), receiving a lot of attention from researchers in recent years. However, its performance in handling MOPs with complicated Pareto fronts (PFs) is still limited, especially for real-world applications whose PFs are often complex featuring, e.g., a long tail or a sharp peak. To deal with this problem, an improved MOEA/D (named iMOEA/D) that mainly focuses on bi-objective optimization problems (BOPS) is therefore proposed in this paper. To demonstrate the capabilities of iMOEA/D, it is applied to design optimization problems of truss structures. In iMOEA/D, the set of the weight vectors defined in MOEA/D is numbered and divided into two subsets: one set with odd-weight vectors and the other with even-weight vectors. Then, a two-phase search strategy based on the MOEND framework is proposed to optimize their corresponding populations. Furthermore, in order to enhance the total performance of iMOEA/D, some recent developments for MOEA/D, including an adaptive replacement strategy and a stopping criterion, are also incorporated. The reliability, efficiency and applicability of iMOEA/D are investigated through seven existing benchmark test functions with complex PFs and three optimal design problems of truss structures. The obtained results reveal that iMOEA/D generally outperforms MOEA/D and NSGA-II in both benchmark test functions and real-world applications. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithm (MOEA) multi-objective evolutionary algorithm based on decomposition (MOEA/D) Complicated Pareto fronts (PFs) Structural optimization Truss structures

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Low emittance lattice optimization using a multi-objective evolutionary algorithm

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Chinese Physics C 2011年第9期35卷 859-864页

作者：高巍巍王琳李为民何多慧 National Synchrotron Radiation Lab University of Science and Technology of China

A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure.

关键词： emittance multi-objective evolutionary algorithm lattice

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multiobjective differential evolution using homeostasis based mutation for application in software cost estimation

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APPLIED INTELLIGENCE 2018年第3期48卷 628-650页

作者： Singh, Shailendra Pratap Kumar, Anoj Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Allahabad Uttar Pradesh India

The problem in software cost estimation revolves around accuracy. To improve the accuracy, heuristic/meta-heuristic algorithms have been known to yield better results when it is applied in the domain of software cost estimation. For the sake of accuracy in results, we are still modifying these algorithms. Here we have proposed a new meta-heuristic algorithm based on Differential Evolution (DE) by Homeostasis mutation operator. Software development requires high prediction and low Root Mean Squared Error (RMSE) and mean magnitude relative error(MMRE). The problem in software cost estimation relates to accurate prediction and minimization of RMSE and MMRE, which are used to solve multiobjective optimization. Many versions of DE were proposed, however multi-objective versions where the concept of Pareto optimality is used, are most popular. Pareto-Based Differential Evolution (PBDE) is one of them. Although the performance of this algorithm is very good, its convergence rate can be further improved by minimizing the time complexity of nondominated sorting, and by improving the diversity of solutions. This has been implemented by using efficient nondominated algorithm whose time complexity is better than the previous one and a new mutation scheme is implemented in DE which can provide more diversity among solutions. The proposed variant multiplies the Homeostasis value with one more vector, named the Homeostasis mutation vector, in the existing mutation vector to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escape the situation of stagnation. The performance of the proposed algorithm is evaluated on twelve benchmark test functions (bi-objective and tri-objective) on the Pareto-optimal front. The performance of the proposed algorithm is compared with other state-of-the-art algorithms on five multi-objective evolutionary algorithms (MOEAs). The result verifies t

关键词： Adaptation Optimization multi-objective evolutionary algorithm Software cost estimation

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Secondary population implementation in multi-objective evolutionary algorithm for scheduling of FMS

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第9-12期57卷 1143-1154页

作者： Pandian, P. Paul Sankar, S. Saravana Ponnambalam, S. G. Bathrinath, S. Monash Univ Petaling Jaya 46150 Selangor Malaysia Kalasalingam Univ Anand Nagar 626190 Krishnankoil India Sethu Inst Technol Pullor 626115 Kariapatti India

Any practical implementation of any multi-objective evolutionary algorithm (MOEA) must include a secondary population composed of all Pareto-optimal solutions found during its search process. Such an implementation with an active participation of solutions from the secondary population into the generational population of the genetic cycle is expected to improve the effectiveness of the MOEA. In this work, two kinds of secondary population, one with set of non-dominated solutions and another with a set of inferior solutions, accrued out of the generation cycles are constructed, and with different combinations of feeding of solutions from these two secondary populations, seven different implementation schemes are designed with an aim of intensifying the convergence and diversification capabilities of the genetic process of MOEA. All the schemes were implemented in a genetic algorithm-based MOEA designed to solve the scheduling problem with dual objectives for a flexible manufacturing system and tested with common experimental data. The performances of the schemes are compared, and the most appropriate implementation scheme is proposed.

关键词： multi-objective evolutionary algorithm Genetic algorithm Secondary population Scheduling optimization Flexible manufacturing system

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Robot path planning based on multi-objective optimization with local search

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第2期35卷 1755-1764页

作者： Xia, Min Zhang, Chong Weng, Liguo Liu, Jia Wang, Ying Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing Jiangsu Peoples R China

Robot path planning is integral to many robotic applications. In this work, three optimization objectives are presented: path length, degree of path smoothness, and degree of security. Due to the lack of local search ability, the optimal solution set is difficult to be obtained with the traditional method especially when the search space is very irregular. And the simple local search algorithm is often trapped into local optimization. A new method with local search is introduced to improve the SPEA2 in this work. The proposed method sets up an external population dedicated to local search, which can increase the local search ability of the method while retaining good global searching ability. In addition, the new crossover operator and the individual update strategy are used for proposed method. The simulation results shows that the proposed method is better than that of SPEA2, NSGA-2 and PESA. It was found that the model proposed in this work is practical for robot path planning.

关键词： multi-objective evolutionary algorithm SPEA2 robot path planning adaptive crossover local search

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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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A multi-objective evolutionary algorithm to Obtain Test Cases With Variable Lengths 11

A Multi-Objective Evolutionary Algorithm to Obtain Test Case...

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13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Yano, Thaise Martins, Eliane de Sousa, Fabiano Luis Univ Estadual Campinas Inst Comp Campinas SP Brazil Natl Inst Space Res Space Mech & Control Div Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781450305570

In this paper a new multi-objective implementation of the generalized extremal optimization (GEO) algorithm, named M-GEO(vsl), is presented. It was developed primarily to be used as a test case generator to find transition paths from extended finite state machines (EFSM), taking into account not only the transition to be covered but also the minimization of the test length. M-GEO(vsl) has the capability to deal with strings whose number of elements vary dynamically, making it possible to generate solutions with different lengths. The steps of the algorithm are described for a general multi-objective problem in which the solution length is an element to be optimized. Experiments were performed to generate test case from EFSM benchmark models using M-GEO(vsl) and the approach was compared with a related work.

关键词： multi-objective evolutionary algorithm model-based testing variable length test case

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Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design

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APPLIED ENERGY 2016年 164卷 140-151页

作者： Mahbub, Md Shahriar Cozzini, Marco Ostergaard, Poul Alberg Alberti, Fabrizio FBK ARES Via Sommar 18 I-38123 Povo Trento Italy Univ Trento Doctoral Sch ICT Via Sommar 9 I-38123 Povo Trento Italy EURAC Res Inst Renewable Energy Viale Druso 1 I-39100 Bolzano Italy Aalborg Univ Dept Dev & Planning Skibbrogade 5 DK-9000 Aalborg Denmark

Environmental and security concerns urge energy planners to design more sustainable energy systems, reducing fossil fuel consumptions in favour of renewable solutions. The proposed scenarios typically rely on a mixing of different energy sources, thereby mitigating the availability and intermittency problems typically related to renewable technologies. Optimizing this combination is of crucial importance to cope with economic, technical, and environmental issues, which typically give rise to multiple contradictory objectives. To this purpose, this article presents a generalized framework coupling EnergyPLAN - a descriptive analytical model for medium/large-scale energy systems - with a multi-objective evolutionary algorithm - a type of optimizer widely used in the context of complex problems. By using this framework, it is possible to automatically identify a set of Pareto-optimal configurations with respect to different competing objectives. As an example, the method is applied to the case of Aalborg municipality, Denmark, by choosing cost and carbon emission minimization as contrasting goals. Results are compared with a manually identified scenario, taken from previous literature. The automatic approach, while confirming that the available manual solution is very close to optimality, yields an entire set of additional optimal solutions, showing its effectiveness in the simultaneous analysis of a wide range of combinations. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Energy systems optimization multi-objective evolutionary algorithm EnergyPLAN modelling Automatic energy scenario design

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Design and Optimisation of a 20kW Horizontal Axis Wind Turbine using HARP_Opt*

Design and Optimisation of a 20kW Horizontal Axis Wind Turbi...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Krishneel A Singh Mohammed R Ahmed School of Engineering and Physics The University of the South Pacific Suva Fiji

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

The design of a 20 kW horizontal axis small wind turbine was carried out using the analysed wind data and the Weibull parameters of a site in Vanuatu. The airfoils were generated for the 20 kW SWT and optimized using XFOIL. The initial airfoil was parameterized using a 7 th order Bezier curve. Three airfoils with thicknesses of 21%, 14% and 9% were generated for the root region, mid region and the tip region respectively. The performance characteristics were studied and Harp_Opt was used to design and optimize the 20 kW wind turbine. A cut-in wind speed of 2 m/s and a rated wind speed of 10 m/s were shown by the power curve. The annual energy production (AEP) of the designed turbine was calculated to be 66.75 MWh/year with a capacity factor of around 38.1%. Small wind turbines will be ideal for the Pacific Island Countries (PICs) since they can operate at low wind speeds and Reynolds numbers less than 500,000. Using larger commercial turbines are not ideal for the PICs due to frequent cyclones and logistics issues thus a SWT is designed in the present work.

关键词： Horizontal axis wind turbine small wind turbine multi objective genetic algorithm HARP_Opt optimization horizontal axis wind turbine Wind Energy Conversion Systems wind speed PICs Weibull parameters multi-objective evolutionary algorithm Reynolds number Standard Widget Toolkit Airfoils Wind data

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Combining Constraint Solving with Different MOEAs for Configuring Large Software Product Lines: A Case Study 42

Combining Constraint Solving with Different MOEAs for Config...

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42nd Annual IEEE-Computer-Society Computers, Software and Applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Yu, Huiqun Shi, Kai Guo, Jianmei Fan, GuiSheng Yang, Xingguang Chen, Liqiong East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai Peoples R China

ISBN: (纸本)9781538626672

multi-objective evolutionary algorithm (MOEA) with the constraint solving has been successfully applied to address the configuration optimization problem in software product line (SPL), for example, the state-of-the-art SATIBEA algorithm. However, each different MOEA with special search operator demonstrates the different strength and weakness in terms of optimality and convergence speed. The SATIBEA just combines the SAT (Boolean satisfiability problem) constraint solving with the Indicator-Based evolutionary algorithm (IBEA) for evaluating the algorithm performance. In this paper, we propose six hybrid algorithms which combine the SAT solving with different MOEAs. Case study is based on five large-scale, rich-constrained and real-world SPLs. Empirical results demonstrate that SATMOCell algorithm obtains a competitive optimization performance to the state-of-the-art that outperforms the SATIBEA in terms of quality Hypervolume metric for 2 out of 5 SPLs within the same time budget. Moreover, the convergence speed of SATMOCell and SATssNSGA2 is comparable after 10min terminal times. Particularly, the Hypervolume value of SATssNSGA2 reports the average improvement of 1.33% after 20min terminal times.

关键词： Constraint solving Search-based software engineering Software product lines multi-objective evolutionary algorithm

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