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A parallel portfolio approach to configuration optimization for large software product lines

SOFTWARE-PRACTICE & EXPERIENCE 2018年第9期48卷 1588-1606页

作者： Shi, Kai Yu, Huiqun Guo, Jianmei Fan, Guisheng Yang, Xingguang East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China Alibaba Grp Hangzhou 311121 Zhejiang Peoples R China

Software product line (SPL) engineering demands for optimal or near-optimal products that balance multiple often competing and conflicting objectives. A major challenge for large SPLs is to efficiently explore a huge space of various products and satisfy a large number of predefined constraints simultaneously. To improve the optimality and convergence speed, we propose a parallel portfolio approach, called IBEAPORT, which designs three algorithm variants by incorporating constraint solving into the indicator-based evolutionary algorithm in different ways and performs these variants by utilizing parallelization techniques. Our approach utilizes the exploration capabilities of different algorithms and improves optimality as far as possible within a limited time budget. We evaluate our approach on five large-scale real-world SPLs. Empirical results demonstrate that our approach significantly outperforms the state of the art for all five SPLs on a quality indicator and a diversity indicator. Moreover, IBEAPORT quickly converges to a relatively stable hypervolume value even for the largest SPL with 6888 features.

关键词： constraint solving multiobjective evolutionary algorithms parallel portfolio search-based software engineering software product lines

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Many objective robust decision making for complex environmental systems undergoing change

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ENVIRONMENTAL MODELLING & SOFTWARE 2013年 42卷 55-71页

作者： Kasprzyk, Joseph R. Nataraj, Shanthi Reed, Patrick M. Lempert, Robert J. Penn State Univ Dept Civil & Environm Engn University Pk PA 16802 USA RAND Corp Santa Monica CA USA

This paper introduces many objective robust decision making (MORDM). MORDM combines concepts and methods from many objective evolutionary optimization and robust decision making (RDM), along with extensive use of interactive visual analytics, to facilitate the management of complex environmental systems. Many objective evolutionary search is used to generate alternatives for complex planning problems, enabling the discovery of the key tradeoffs among planning objectives. RDM then determines the robustness of planning alternatives to deeply uncertain future conditions and facilitates decision makers' selection of promising candidate solutions. MORDM tests each solution under the ensemble of future extreme states of the world (SOW). Interactive visual analytics are used to explore whether solutions of interest are robust to a wide range of plausible future conditions (i.e., assessment of their Pareto satisficing behavior in alternative SOW). Scenario discovery methods that use statistical data mining algorithms are then used to identify what assumptions and system conditions strongly influence the cost-effectiveness, efficiency, and reliability of the robust alternatives. The framework is demonstrated using a case study that examines a single city's water supply in the Lower Rio Grande Valley (LRGV) in Texas, USA. Results suggest that including robustness as a decision criterion can dramatically change the formulation of complex environmental management problems as well as the negotiated selection of candidate alternatives to implement. MORDM also allows decision makers to characterize the most important vulnerabilities for their systems, which should be the focus of ex post monitoring and identification of triggers for adaptive management. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multiobjective evolutionary algorithms Robust decision making Interactive visual analytics Uncertainty analysis Water supply Environmental management

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A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks

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APPLIED SOFT COMPUTING 2012年第7期12卷 1891-1901页

作者： Vecchio, Massimo Lopez-Valcarce, Roberto Marcelloni, Francesco Univ Vigo Dept Teoria Senal & Comunicac Vigo 36310 Spain Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

To know the location of nodes plays an important role in many current and envisioned wireless sensor network applications. In this framework, we consider the problem of estimating the locations of all the nodes of a network, based on noisy distance measurements for those pairs of nodes in range of each other, and on a small fraction of anchor nodes whose actual positions are known a priori. The methods proposed so far in the literature for tackling this non-convex problem do not generally provide accurate estimates. The difficulty of the localization task is exacerbated by the fact that the network is not generally uniquely localizable when its connectivity is not sufficiently high. In order to alleviate this drawback, we propose a two-objective evolutionary algorithm which takes concurrently into account during the evolutionary process both the localization accuracy and certain topological constraints induced by connectivity considerations. The proposed method is tested with different network configurations and sensor setups, and compared in terms of normalized localization error with another metaheuristic approach, namely SAL, based on simulated annealing. The results show that, in all the experiments, our approach achieves considerable accuracies and significantly outperforms SAL, thus manifesting its effectiveness and stability. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks Node localization Range measurements Stochastic optimization multiobjective evolutionary algorithms

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Optimizing the Door Assignment in LTL-Terminals

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TRANSPORTATION SCIENCE 2009年第2期43卷 198-210页

作者： Chmielewski, Annette Naujoks, Boris Janas, Michael Clausen, Uwe TU Dortmund Univ D-44221 Dortmund Germany Login GmbH D-58332 Schwelm Germany

In less-than-truckload (LTL) terminals, arriving trucks have to be assigned to inbound doors and to suitable time slots for unloading. Simultaneously, waiting trucks have to be allocated to outbound doors. During a couple of hours, shipments from all incoming trucks are unloaded, sorted according to their relation, transported to the right outbound door, and loaded on the outgoing truck. (The term "relation" is an equivalent for destination;it originates from the German logistics vocabulary that uses the term to specify a certain transport offered between a source and a sink.) The first and the most important optimization aim is to minimize the total distance when transshipping units, because this leads to reduction in operational costs, which are usually very high. The second, and minor, aim is to minimize the waiting time for each truck. Usually the operator of an LTL transshipment building works with subcontractors when collecting and delivering goods. Therefore, no penalties have to be paid by the operators in case waiting times are too long. The logistical optimization task is modeled as a time-discrete, multicommodity flow problem with side constraints. Based on the applicable model, a decomposition approach and a modified column-generation approach are developed. In parallel, an evolutionary algorithm (EA) was implemented to tackle the problem at hand. Both algorithms-from the field of discrete mathematics, as well as from the field of computational intelligence-are applied to 10 test scenarios. A comparison of the solution process, as well as a comparison of the solution quality, concludes the work.

关键词： door assignment column generation multiobjective evolutionary algorithms

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A comparison of multi-objective optimization algorithms for weight setting problems in traffic engineering

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NATURAL COMPUTING 2022年第3期21卷 507-522页

作者： Pereira, Vitor Sousa, Pedro Rocha, Miguel Univ Minho Ctr Biol Engn Dept Informat Braga Portugal Univ Minho Dept Informat Ctr Algoritmi Braga Portugal

Traffic engineering approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, setting appropriate weights to the links is an important and challenging optimization task. Different approaches have been put forward towards this aim, including evolutionary algorithms (EAs). This work addresses the evaluation of a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the network, with (1) changes in the traffic demand matrices, and (2) link failures. The methods will simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach. Since this leads to a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally;those are compared to a single-objective EA previously proposed by the authors. The results show a remarkable performance and scalability of NSGA-II in the proposed tasks presenting itself as the most promising option for TE.

关键词： Traffic engineering Intra-domain routing Link-state routing protocols multiobjective evolutionary algorithms NSGA-II

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Deriving Water Allocation Schemes for Interbasin Water Transfer Projects Using a Novel multiobjective Cuckoo Search Algorithm

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2024年第6期150卷 04024016-04024016页

作者： Zhong, Huayu Liao, Tao Fang, Guohua Zhang, Shiwei Zhou, Bingyi Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Jiangsu Prov Water Conservancy Survey & Design Ins Planning Dept Yangzhou 225000 Peoples R China

Interbasin water transfer projects (IBWTPs) have become an important measure to alleviate regional water stresses by artificially regulating water resources between water-rich and water-scarce areas. However, there are many conflicting objectives and complex practical operational constraints that complicate the derivation of water allocation schemes for IBWTPs. Here, we propose a multiobjective optimization methodology for deriving water allocation schemes for IBWTPs, which includes three modules: (1) formulating a multiobjective optimal water allocation problem for IBWTPs based on practical operational constraints;(2) proposing a novel multiobjective cuckoo search (NMOCS) algorithm and combining it with a simulation-optimization approach to solve the water allocation problem;and (3) filtering the Pareto solutions using the analytic hierarchy process (AHP)-entropy method. Based on the multiobjective cuckoo search (MOCS) algorithm, the NMOCS employs four improvement strategies, including a population initialization strategy, flock search strategy, multistrategy external archive maintenance strategy, and adaptive parameters, to improve the convergence property and diversity of solutions. To test the performance of the NMOCS, we considered the MOCS and four widely used multiobjective evolutionary algorithms (MOEAs) as a comparison and employed these six MOEAs to solve 11 multiobjective mathematical benchmark problems as well as the water allocation problems of the Jiangsu Province, China, section of the South-to-North Water Diversion Project under normal, dry, and extremely dry hydrological conditions. The results show that the NMOCS outperformed other MOEAs in handling multiobjective mathematical benchmark problems, especially in ZDT1, ZDT2, ZDT6, DTLZ2, DTLZ4, and DTLZ7. Compared with other MOEAs, the NMOCS did not always capture the highest percentage of the reference water allocation schemes, but it provided more than 18% (greater than one-sixth) of effective water

关键词： multiobjective optimization multiobjective evolutionary algorithms External archive maintenance Multicriteria decision making Jiangsu Province, China, section of the South-to-North Water Diversion Project

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Handling multiple objectives using k-means clustering guided multiobjective evolutionary algorithm

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EXPERT SYSTEMS 2022年第4期39卷

作者： Singh, Tribhuvan Siksha O Anusandhan Deemed Be Univ Dept Comp Sci & Engn Bhubaneswar Odisha India

multiobjective optimization problems (MOPs) are very popular these days and have gained continuous research attention. These problems involve a minimum of two conflicting objectives that need to be optimized simultaneously. multiobjective evolutionary algorithms (MOEAs) optimize these objectives by targeting solutions on the Pareto front. To judge the performance of different MOEAs, the convergence rate and population diversity are used. In order to create a good algorithm with a high convergence rate and diversity, the k-means clustering algorithm is combined with a real parameter version of the improved environmental adaptation method (IEAM-RP) in this paper. This is done as IEAM-RP converges very fast and the k-means clustering algorithm is used to provide diversity in decision search space. To check the performance of the proposed algorithm, it has been compared with nine state-of-the-art multiobjective algorithms (MOPSOtridist, MOEA/D-STM, MOEA/D, MOPSO, NSGA-II, MOPSOses, MOGOA, MODA, MOALO) using standard multiobjective benchmark functions. Further, the performance of the proposed algorithm is compared against IBEA and SMS-EMOA using walking fish group (WFG) test suit. The experimental results prove the effectiveness of the proposed algorithm.

关键词： environmental adaptation method evolutionary computation k-means clustering multiobjective evolutionary algorithms

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multiobjective algorithms WITH RESAMPLING FOR PORTFOLIO OPTIMIZATION

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COMPUTING AND INFORMATICS 2013年第4期32卷 777-796页

作者： Garcia, Sandra Quintana, David Galvan, Ines M. Isasi, Pedro Univ Carlos III Madrid Dept Comp Sci Leganes 28911 Spain

Constrained financial portfolio optimization is a challenging domain where the use of multiobjective evolutionary algorithms has been thriving over the last few years. One of the major issues related to this problem is the dependence of the results on a set of parameters. Given the nature of financial prediction, these figures are often inaccurate, which results in unreliable estimates for the efficient frontier. In this paper we introduce a resampling mechanism that deals with uncertainty in the parameters and results in efficient frontiers that are more robust. We test this idea on real data using four multiobjective optimization algorithms (NSGA-II, GDE3, SMPSO and SPEA2). The results show that resampling significantly increases the reliability of the resulting portfolios.

关键词： Financial portfolio optimization robust portfolio multiobjective evolutionary algorithms

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DpEA: A dual-population evolutionary algorithm for dynamic constrained multiobjective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartA期255卷

作者： Yang, Cuicui Sui, Guangyuan Ji, Junzhong Li, Xiang Zhang, Xiaoyu Beijing Univ Technol Coll Comp Sci Beijing Municipal Key Lab Multimedia & Intelligent Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China

Dynamic Constrained multiobjective Optimization Problems (DCMOPs) are very difficult to solve because both of the objectives and constraints may change over time. The existing approaches for solving DCMOPs mainly develop dynamic response techniques and constraint handling techniques. But they do not focus on the search capability of the static optimizer in each environment, which ignores the intrinsic requirement of quickly locating Pareto-optimal Front (PF) in each environment when solving DCMOPs. To this end, this paper proposes a dual -population evolutionary algorithm for solving DCMOPs, called as DpEA, which maintains a population without considering constraints (called UP) for exploration and a population with considering constraints (called CP) for exploitation in each environment. In each iteration of a new environment, UP firstly adopts a stratified mutation strategy (SMS) and a dominated solution repairment strategy (DSR) to enhance the exploration ability of finding promising regions where the PF may reside. SMS uses solutions from different nondominated fronts to generate offspring, while DSR repairs the single -optimal variables of the dominated solutions by sampling from the distribution of those variables of nondominated solutions. Secondly, this paper uses an adaptive offspring ratio adjustment strategy to control the offspring number generated by UP and CP according to the normalized Hausdorff distance between nondominated solution sets from the two latest generations of UP. This strategy is helpful to balance the intensity between exploration and exploitation and thereby ensures efficient search. Experimental results on CEC 2023 DCF test suite show that DpEA has a superior performance over six state -of -the -art algorithms.

关键词： Dynamic constrained multiobjective optimization problems multiobjective evolutionary algorithms Stratified mutation Dominated solution repairment Offspring generation ratio

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Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2013年第4期68卷 197-224页

作者： Sato, Hiroyuki Aguirre, Hernan Tanaka, Kiyoshi Univ Electrocommun Fac Informat & Engn Chofu Tokyo 1828585 Japan Shinshu Univ Fac Engn Nagano 3808553 Japan

In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA(is an element of+) and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with n = {100, 250, 500, 750, 1,000} items (bits) and m = {2, 4, 6, 8, 10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for m = 4 objectives and that their effectiveness becomes larger as the number of objectives m increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed.

关键词： multiobjective evolutionary algorithms Many-objective optimization Many-objective 0/1 knapsack problem Local recombination Controlling the number of crossed genes

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