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multi-objective reverse engineering of variability-safe feature models based on code dependencies of system variants

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EMPIRICAL SOFTWARE ENGINEERING 2017年第4期22卷 1763-1794页

作者： Assuncao, Wesley K. G. Lopez-Herrejon, Roberto E. Linsbauer, Lukas Vergilio, Silvia R. Egyed, Alexander Fed Univ Parana UFPR DINF CP 19081 BR-81531980 Curitiba Parana Brazil Fed Univ Technol Parana UTFPR COTSI Cristo Rei St 19 BR-85902490 Toledo Brazil ETS Dept Software Engn & IT Notre Dame St Ouest 1100 Montreal PQ H3C 1K3 Canada Johannes Kepler Univ Linz JKU ISSE Altenbergerstr 69 A-4040 Linz Austria

Maintenance of many variants of a software system, developed to supply a wide range of customer-specific demands, is a complex endeavour. The consolidation of such variants into a Software Product Line is a way to effectively cope with this problem. A crucial step for this consolidation is to reverse engineer feature models that represent the desired combinations of features of all the available variants. Many approaches have been proposed for this reverse engineering task but they present two shortcomings. First, they use a single-objective perspective that does not allow software engineers to consider design trade-offs. Second, they do not exploit knowledge from implementation artifacts. To address these limitations, our work takes a multi-objective perspective and uses knowledge from source code dependencies to obtain feature models that not only represent the desired feature combinations but that also check that those combinations are indeed well-formed, i.e. variability safe. We performed an evaluation of our approach with twelve case studies using NSGA-II and SPEA2, and a single-objective algorithm. Our results indicate that the performance of the multi-objective algorithms is similar in most cases and that both clearly outperform the single-objective algorithm. Our work also unveils several avenues for further research.

关键词： Reverse engineering Feature models multi-objective evolutionary algorithms Empirical evaluation

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A New Algorithm Using the Non-Dominated Tree to Improve Non-Dominated Sorting

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evolutionary COMPUTATION 2018年第1期26卷 89-116页

作者： Gustavsson, Patrik Syberfeldt, Anna Univ Skovde Sch Engn S-54134 Skovde Sweden

Non-dominated sorting is a technique often used in evolutionary algorithms to determine the quality of solutions in a population. The most common algorithm is the Fast Non-dominated Sort (FNS). This algorithm, however, has the drawback that its performance deteriorates when the population size grows. The same drawback applies also to other non-dominating sorting algorithms such as the Efficient Non-dominated Sort with Binary Strategy (ENS-BS). An algorithm suggested to overcome this drawback is the Divide-and-Conquer Non-dominated Sort (DCNS) which works well on a limited number of objectives but deteriorates when the number of objectives grows. This article presents a new, more efficient algorithm called the Efficient Non-dominated Sort with Non-Dominated Tree (ENS-NDT). ENS-NDT is an extension of the ENS-BS algorithm and uses a novel Non-Dominated Tree (NDTree) to speed up the non-dominated sorting. ENS-NDT is able to handle large population sizes and a large number of objectives more efficiently than existing algorithms for non-dominated sorting. In the article, it is shown that with ENS-NDT the runtime of multi-objective optimization algorithms such as the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) can be substantially reduced.

关键词： evolutionary computation k-d tree multi-objective evolutionary algorithms non-dominated sorting Pareto optimality runtime complexity

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Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

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ENGINEERING OPTIMIZATION 2014年第8期46卷 1032-1051页

作者： Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen Thailand

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.

关键词： comparative performance multi-objective evolutionary algorithms multi-objective truss optimization hybrid evolutionary algorithms population-based incremental learning

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A multi-objective differential evolution algorithm based on domination and constraint-handling switching

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INFORMATION SCIENCES 2021年 579卷 796-813页

作者： Yang, Yongkuan Liu, Jianchang Tan, Shubin Liu, Yuanchao Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Northeastern Univ Dept Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Many domination-based multi-objective evolutionary algorithms (MOEAs) are designed for constrained multi-objective optimization problems (CMOPs). However, they still face the challenge of balancing the feasibility, convergence and distribution. This paper tackles this issue by proposing a multi-objective differential evolution algorithm based on domination and a mechanism of constraint-handling switching (MODE-CHS). In constraint handling switching, if there are no feasible solutions in the population, the population evolves by constraint-handling;otherwise, the population evolves without handling constraints. This mechanism enhances the rate of population convergence to the maximum while obtaining the feasible solutions. Furthermore, in MODE-CHS, the feasible solutions are saved to an external archive and evolve together with the population to explore the feasible region. Meanwhile, to enhance the distribution, the offspring of the external archive also participates in the individual-update procedure of the population. 27 bench-mark test problems and two real-world problems are used for the performance comparison of the proposed algorithm with other five state-of-the-art algorithms. In the experiment, the proposed algorithm, MODE-CHS, is shown to produce satisfactory solutions for most of the tested functions, where other five MOEAs perform relatively worse. The experiment results demonstrate MODE-CHS is very competitive for solving CMOPs. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithms Constraint-handling technology Differential evolution Constrained multi-objective optimization problems

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An iterative approach to multi-objective engineering design: Optimization of engineered injection and extraction for enhanced groundwater remediation

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ENVIRONMENTAL MODELLING & SOFTWARE 2015年 69卷 253-261页

作者： Piscopo, A. N. Kasprzyk, J. R. Neupauer, R. M. Univ Colorado Dept Civil Environm & Architectural Engn Boulder CO 80309 USA

This study contributes an iterative problem reformulation technique for multi-objective evolutionary algorithm (MOEA) decision support. Problem formulations consist of objectives, decision variables, and constraints, and directly influence the results generated by the MOEA. Typically, design problems are optimized based on a single problem formulation established a priori. In this paper, we demonstrate an approach to perform iterative optimization using problem formulations updated from analyses of results from prior rounds of optimization, which often reveal design components not initially considered. To demonstrate the approach, we consider a novel groundwater remediation technique, Engineered Injection and Extraction (EIE), which has never been optimized in the literature. Iterative problem reformulation enabled the MOEA to generate EIE solutions with better performance than the heuristically-developed solution used in prior work. Published by Elsevier Ltd.

关键词： multi-objective optimization multi-objective evolutionary algorithms Groundwater remediation Problem formulation Visualization

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Spatial and sectoral benefit distribution in water-energy system design

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APPLIED ENERGY 2020年 269卷 114794-114794页

作者： Gonzalez, Jose M. Tomlinson, James E. Harou, Julien J. Cesena, Eduardo A. Martinez Panteli, Mathaios Bottacin-Busolin, Andrea Hurford, Anthony Olivares, Marcelo A. Siddiqui, Afzal Erfani, Tohid Strzepek, Kenneth M. Mancarella, Pierluigi Mutale, Joseph Obuobie, Emmanuel Seid, Abdulkarim H. Ya, Aung Ze Univ Manchester Dept Mech Aerosp & Civil Engn Manchester M13 9PL Lancs England Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England Univ Chile Dept Civil Engn Santiago 2002 Chile UCL Dept Stat Sci London WC1E 6BT England Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden UCL Dept Civil Environm & Geomat Engn London WC1E 6BT England MIT Joint Program Sci & Policy Global Change 77 Massachusetts AveE19-429F Cambridge MA 02139 USA Univ Melbourne Sch Elect & Elect Engn Melbourne Vic Australia CSIR Water Res Inst POB AH 38 Achimota Accra Ghana Nile Basin Initiat Secretariat Water Resources Management Entebbe Uganda Yangon Technol Univ Dept Elect Power Engn Insein Township Yangon Myanmar

The design of water and energy systems has traditionally been done independently or considering simplified interdependencies between the two systems. This potentially misses valuable synergies between them and does not consider in detail the distribution of benefits between different sectors or regions. This paper presents a framework to couple integrated water-power network simulators with multi-objective optimisation under uncertainty to explore the implications of explicitly including spatial topology and interdependencies in the design of multi-sector integrated systems. A synthetic case study that incorporates sectoral dependencies in resource allocation, operation of multi-purpose reservoirs and spatially distributed infrastructure selection in both systems is used. The importance of explicitly modelling the distribution of benefits across different sectors and regions is explored by comparing different spatially aggregated and disaggregated multi-objective optimisation formulations. The results show the disaggregated formulation identifies a diverse set of non-dominated portfolios that enables addressing the spatial and sectoral distribution of benefits, whilst the aggregated formulations arbitrarily induce unintended biases. The proposed disaggregated approach allows for detailed spatial design of interlinked water and energy systems considering their complex regional and sectoral trade-offs. The framework is intended to assist planners in real resource systems where diverse stakeholder groups are mindful of receiving their fair share of development benefits.

关键词： Water-energy system design multi-sector benefit distribution multi-objective robust optimisation under uncertainty multi-objective evolutionary algorithms

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Robust optimization to secure urban bulk water supply against extreme drought and uncertain climate change

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ENVIRONMENTAL MODELLING & SOFTWARE 2015年 69卷 437-451页

作者： Mortazavi-Naeini, Mohammad Kuczera, George Kiem, Anthony S. Cui, Lijie Henley, Benjamin Berghout, Brendan Turner, Emma Univ Newcastle Callaghan NSW 2308 Australia Hunter Water Corp Newcastle NSW Australia

Urban bulk water systems supply water with high reliability and, in the event of extreme drought, must avoid catastrophic economic and social collapse. In view of the deep uncertainty about future climate change, it is vital that robust solutions be found that secure urban bulk water systems against extreme drought. To tackle this challenge an approach was developed integrating;1) a stochastic model of multisite streamflow conditioned on future climate change scenarios;2) Monte Carlo simulation of the urban bulk water system incorporated into a robust optimization framework and solved using a multi-objective evolutionary algorithm;and 3) a comprehensive decision space including operating rules, investment in new sources and source substitution and a drought contingency plan with multiple actions with increasingly severe economic and social impact. A case study demonstrated the feasibility of this approach for a complex urban bulk water supply system. The primary objective was to minimize the expected present worth cost arising from infrastructure investment, system operation and the social cost of "normal" and emergency restrictions. By introducing a second objective which minimizes either the difference in present worth cost between the driest and wettest future climate change scenarios or the present worth cost for driest climate scenario, the trade-off between efficiency and robustness was identified. The results show that a significant change in investment and operating strategy can occur when the decision maker expresses a stronger preference for robustness and that this depends on the adopted robustness measure. Moreover, solutions are not only impacted by the degree of uncertainty about future climate change but also by the stress imposed on the system and the range of available options. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Climate change Deep uncertainty Robust optimization Urban water resource management multi-objective evolutionary algorithms

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Dynamic Software Project Scheduling through a Proactive-Rescheduling Method

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2016年第7期42卷 658-686页

作者： Shen, Xiaoning Minku, Leandro L. Bahsoon, Rami Yao, Xin Nanjing Univ Informat Sci & Technol Sch Informat & Control B DAT & CICAEET 219 Ning Liu Rd Nanjing 210044 Jiangsu Peoples R China Univ Leicester Dept Comp Sci Univ Rd Leicester LE1 7RH Leics England Univ Birmingham CERCIA Birmingham B15 2TT W Midlands England

Software project scheduling in dynamic and uncertain environments is of significant importance to real-world software development. Yet most studies schedule software projects by considering static and deterministic scenarios only, which may cause performance deterioration or even infeasibility when facing disruptions. In order to capture more dynamic features of software project scheduling than the previous work, this paper formulates the project scheduling problem by considering uncertainties and dynamic events that often occur during software project development, and constructs a mathematical model for the resulting multi-objective dynamic project scheduling problem (MODPSP), where the four objectives of project cost, duration, robustness and stability are considered simultaneously under a variety of practical constraints. In order to solve MODPSP appropriately, a multi-objective evolutionary algorithm based proactive-rescheduling method is proposed, which generates a robust schedule predictively and adapts the previous schedule in response to critical dynamic events during the project execution. Extensive experimental results on 21 problem instances, including three instances derived from real-world software projects, show that our novel method is very effective. By introducing the robustness and stability objectives, and incorporating the dynamic optimization strategies specifically designed for MODPSP, our proactive-rescheduling method achieves a very good overall performance in a dynamic environment.

关键词： Schedule and organizational issues dynamic software project scheduling search-based software engineering multi-objective evolutionary algorithms mathematical modeling

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Entropy-based evaluation function in a multi-objective approach for the investigation of the genetic code robustness

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MEMETIC COMPUTING 2014年第3期6卷 157-170页

作者： de Oliveira, Lariza Laura Tinos, Renato Univ Sao Paulo Dept Comp & Math BR-14049 Ribeirao Preto Brazil

The genetic code is the interface between the genetic information stored in DNA molecules and the proteins. Considering the hypothesis that the genetic code evolved to its current structure, some researches use optimization algorithms to find hypothetical codes to be compared to the canonical genetic code. For this purpose, a function with only one objective is employed to evaluate the codes, generally a function based on the robustness of the code against mutations. Very few random codes are better than the canonical genetic code when the evaluation function based on robustness is considered. However, most codons are associated with a few amino acids in the best hypothetical codes when only robustness is employed to evaluate the codes, what makes hard to believe that the genetic code evolved based on only one objective, i.e., the robustness against mutations. In this way, we propose here to use entropy as a second objective for the evaluation of the codes. We propose a Pareto approach to deal with both objectives. The results indicate that the Pareto approach generates codes closer to the canonical genetic code when compared to the codes generated by the approach with only one objective employed in the literature.

关键词： Genetic code robustness Genetic algorithms Computational biology multi-objective evolutionary algorithms

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Many objective Particle Swarm Optimization

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INFORMATION SCIENCES 2016年第0期374卷 115-134页

作者： Figueiredo, E. M. N. Ludermir, T. B. Bastos-Filho, C. J. A. Univ Fed Pernambuco Ctr Informat Rua Estrela 52 BR-52060160 Recife PE Brazil Univ Pernambuco Escola Politecn Pernambuco Recife PE Brazil

Many-objective problems refer to the optimization problems containing more than three conflicting objectives. To obtain a representative set of well-distributed non-dominated solutions close to Pareto front in the objective space remains a challenging problem. Many papers have proposed different multi-objective evolutionary algorithms to solve the lack of the convergence and diversity in many-objective problems. One of the more promising approaches uses a set of reference points to discriminate the solutions and guide the search process. However, this approach was incorporated mainly in multi-objective evolutionary algorithms, and there are just some few promising adaptations of Particle Swarm Optimization approaches for effectively tackling many-objective problems regarding convergence and diversity. Thus, this paper proposes a practical and efficient Many-objective Particle Swarm Optimization algorithm for solving many-objective problems. Our proposal uses a set of reference points dynamically determined according to the search process, allowing the algorithm to converge to the Pareto front, but maintaining the diversity of the Pareto front. Our experimental results demonstrate superior or similar performance when compared to other state-of-art algorithms. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Many-objective problems multi-objective evolutionary algorithms Particle Swarm Optimization Swarm intelligence

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