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Impact of selection methods on the diversity of many-objective Pareto set approximations

Procedia Computer Science 2017年 112卷 844-853页

作者： Luis Martí Eduardo Segredo Nayat S´nchez-Pi Emma Hart Institute of Computing Universidade Federal Fluminense Niteroi (RJ) Brazil TAU team CNRS/INRIA/LRI Universite Paris-Saclay Paris France School of Computing Edinburgh Napier University Edinburgh Scotland UK Dpto. Ingeniería Informática y de Sistemas Universidad de La Laguna San Cristobal de La Laguna Spain DICC/IME Rio de Janeiro State University Rio de Janeiro (RJ) Brazil

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator-based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hypervolume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, yield the best results.

关键词： multi-objective evolutionary algorithm many-objective optimisation selection mechanism diversity preservation

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Bi-objective optimization for integrating production and preventive maintenance scheduling in two-stage assembly flow shop problem

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2016年第6期33卷 404-425页

作者： Seidgar, Hany Zandieh, Mostafa Mahdavi, Iraj Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Shahid Beheshti Univ GC Management & Accounting Fac Dept Ind Management Tehran Iran

This paper presents a new bi-objective mixed integer programming model for the two-stage assembly flow shop scheduling problem with preventive maintenance (PM) activities, in which the reliability/availability approach is employed to model the maintenance concepts of a problem. PM activities carry out the operations on machines and tools before the breakdown takes place. Therefore, it helps to prevent failures before they happen. After developing a new bi-objective model, an Epsilon-constraint method is proposed to solve the problem. This problem has been known as Np-hard. Therefore, three multi-objective optimization methods, namely fast non-dominated sorting genetic algorithm, multi-objective imperialist competitive algorithm, and non-dominated ranking genetic algorithm (NRGA) are employed to find the pareto-optimal front for large sized problems. The parameters of the proposed algorithms are calibrated using artificial neural network (ANN) and the performances of the proposed algorithms on the problems of various sizes are analyzed and the computational results reveal that NRGA outperform than two other proposed algorithms in quality of solutions and computational time.

关键词： Two-stage assembly flow shop production scheduling preventive maintenance reliability/unavailability approach multi-objective evolutionary algorithm

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multi-objective Evolution of Machine Learning Workflows

Multi-Objective Evolution of Machine Learning Workflows

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IEEE Symposium Series on Computational Intelligence

作者： Tomas Kren Martin Pilat Roman Neruda Faculty of Mathematics and Physics Charles university Prague Czech republic Institute of Computer Science Czech Academy of Sciences Prague Czech republic

In this paper we describe a multi-objective genetic programming algorithm which can be used to create complete machine learning workflows. The algorithm is an extension of a single-objective one. In a series of test on four datasets, we show that the additional objectives can be used to search for smaller or faster models. The algorithm is also in some cases much faster than the single-objective one while obtaining results of similar quality.

关键词： Genetic programming multi-objective evolutionary algorithm Machine learning

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A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition

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PATTERN RECOGNITION 2016年 58卷 172-189页

作者： Sarkhel, Ritesh Das, Nibaran Saha, Amit K. Nasipuri, Mita Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

Identifying the most informative local regions of a handwritten character image is necessary for a robust handwritten character recognition system. But identifying them from a character image is a difficult task. If this task were to be performed incurring minimum possible cost, it becomes more challenging due to having two independent, apparently contradicting objectives which need to be optimized simultaneously, i.e. maximizing the recognition accuracy and minimizing the associated recognition cost. To address the problem a multi-objective approach is required. In the present task, two popular multi objective optimization algorithm (1) a Non-Dominated Sorting Harmony-Search algorithm (NSHA) and (2) a Non-Dominated Sorting Genetic algorithm-II (NSGA-Il, Deb et al., 2002 [18]) are employed for region sampling separately. The method objectively selects the most informative set of local regions using the framework of Axiomatic Fuzzy Set (AFS) theory, from the sets of pareto-optimal solutions provided by the multi-objective region sampling algorithms. The system has been evaluated on two isolated handwritten Bangla datasets, (1) a dataset of randomly mixed handwritten Bangla Basic and Compound characters and (2) a dataset of handwritten Bangla numerals separately, with SVM based classifier, using a feature set containing convex-hull based features and CG based quad-tree partitioned longest-run based local features extracted from the selected local regions. The results have shown a significant increase in recognition accuracy and decrease in recognition cost for all the datasets. Thus the present system introduces a cost effective approach towards isolated handwritten character recognition systems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Feature set Region sampling Handwritten character recognition multi-objective evolutionary algorithm Harmony search NSGA-II AFS theory

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Prior knowledge guided active modules identification: an integrated multi-objective approach

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BMC SYSTEMS BIOLOGY 2017年第Sup2期11卷 8-8页

作者： Chen, Weiqi Liu, Jing He, Shan Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

Background: Active module, defined as an area in biological network that shows striking changes in molecular activity or phenotypic signatures, is important to reveal dynamic and process-specific information that is correlated with cellular or disease states. Methods: A prior information guided active module identification approach is proposed to detect modules that are both active and enriched by prior knowledge. We formulate the active module identification problem as a multi-objective optimisation problem, which consists two conflicting objective functions of maximising the coverage of known biological pathways and the activity of the active module simultaneously. Network is constructed from protein-protein interaction database. A beta-uniform-mixture model is used to estimate the distribution of p-values and generate scores for activity measurement from microarray data. A multi-objective evolutionary algorithm is used to search for Pareto optimal solutions. We also incorporate a novel constraints based on algebraic connectivity to ensure the connectedness of the identified active modules. Results: Application of proposed algorithm on a small yeast molecular network shows that it can identify modules with high activities and with more cross-talk nodes between related functional groups. The Pareto solutions generated by the algorithm provides solutions with different trade-off between prior knowledge and novel information from data. The approach is then applied on microarray data from diclofenac-treated yeast cells to build network and identify modules to elucidate the molecular mechanisms of diclofenac toxicity and resistance. Gene ontology analysis is applied to the identified modules for biological interpretation. Conclusions: Integrating knowledge of functional groups into the identification of active module is an effective method and provides a flexible control of balance between pure data-driven method and prior information guidance.

关键词： Prior knowlege multi-objective evolutionary algorithm Active module identification

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Uncertainty-Based multi-objective Decision Making with Hierarchical Reliability Analysis Under Water Resources and Environmental Constraints

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WATER RESOURCES MANAGEMENT 2016年第2期30卷 805-822页

作者： Dong, Feifei Liu, Yong Su, Han Liang, Zhongyao Zou, Rui Guo, Huaicheng Peking Univ Coll Environm Sci & Engn Key Lab Water & Sediment Sci MOE Beijing 100871 Peoples R China Tetra Tech Inc 10306 Eaton PlSte 340 Fairfax VA 22030 USA Yunnan Key Lab Pollut Proc & Management Plateau L Kunming 650034 Peoples R China

Rapid urbanization and population growth have resulted in worldwide serious water shortage and environmental deterioration. It is then essential for efficient and feasible allocation of scarce water and environment resources to the competing users. Due to inherent uncertainties, decision making for resources allocation is vulnerable to failure. The scheme feasibility can be evaluated by reliability, representing the failure probability. A progressive reliability oriented multi-objective (PROMO) optimal decision-making procedure is proposed in this study to deal with problems with numerous reliability objectives. Dimensionality of the objectives is reduced by a top-down hierarchical reliability analysis (HRA) process combining optimization with evaluation. Pareto solutions of the reformulated model, representing alternative schemes non-dominated with each other, are generated by a metalmodel-based optimization algorithm. Evaluation and identification of Pareto solutions are conducted by multi-criteria decision analysis (MCDA). The PROMO procedure is demonstrated for a case study on industrial structure transformation under strict constraints of water resources and total environmental emissions amounts in Guangzhou City, South China. The Pareto front reveals tradeoffs between economic returns of the industries and system reliability. For different reliability preference scenarios, the Pareto solutions are ranked and the top-rated one was recommended for implementation. The model results indicate that the PROMO procedure is effective for model solving and scheme selection of uncertainty-based multi-objective decision making.

关键词： Water resources allocation Stochastic programming Tradeoff analysis multi-criteria decision analysis multi-objective evolutionary algorithm Optimization

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objective reduction based on nonlinear correlation information entropy

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SOFT COMPUTING 2016年第6期20卷 2393-2407页

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

It is hard to obtain the entire solution set of a many-objective optimization problem (MaOP) by multi-objective evolutionary algorithms (MOEAs) because of the difficulties brought by the large number of objectives. However, the redundancy of objectives exists in some problems with correlated objectives (linearly or nonlinearly). objective reduction can be used to decrease the difficulties of some MaOPs. In this paper, we propose a novel objective reduction approach based on nonlinear correlation information entropy (NCIE). It uses the NCIE matrix to measure the linear and nonlinear correlation between objectives and a simple method to select the most conflicting objectives during the execution of MOEAs. We embed our approach into both Pareto-based and indicator-based MOEAs to analyze the impact of our reduction method on the performance of these algorithms. The results show that our approach significantly improves the performance of Pareto-based MOEAs on both reducible and irreducible MaOPs, but does not much help the performance of indicator-based MOEAs.

关键词： multi-objective optimization objective reduction Nonlinear correlation information entropy multi-objective evolutionary algorithm Dimension reduction

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An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

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ADVANCES IN ENGINEERING SOFTWARE 2016年第0期99卷 161-176页

作者： Lu, Chao Xiao, Shengqiang Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

This paper aims to provide a solution method for a real-world scheduling case from a welding process, which is one of the important processes in modern industry. The unique characteristic of the welding scheduling problem (WSP) is that multiple machines can process one operation at a time. Thus, WSP is a new scheduling problem. We first formulate a new multi-objective mixed integer programming model for this WSP based on a comprehensive investigation. This model involves some realistic constraints, controllable processing times (CPT), sequence dependent setup times (SDST) and job dependent transportation times (JDTT). Then we propose a multi-objective discrete grey wolf optimizer (MODGWO) considering not only production efficiency but also machine load on this real-world scheduling case. The solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. A reduction machine load strategy is used to adjust the number of machines aiming to minimize the machine load. To evaluate the effectiveness of the proposed MODGWO, we compare it with other well-known multi-objective evolutionary algorithms including NSGA-II and SPEA2 on a set of instances. Experimental results demonstrate that the proposed MODGWO is superior to the compared algorithms in terms of convergence, spread and coverage on most instances. Finally, MODGWO is successfully applied to this real-world WSP. This implies that the proposed model is feasible and the proposed algorithm can solve this real-world scheduling problem very well. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Welding scheduling multi-objective evolutionary algorithm Controllable processing times Sequence dependent setup times Transportation times Grey wolf optimizer

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Incorporating domain knowledge into the optimization of energy systems

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APPLIED SOFT COMPUTING 2016年第0期47卷 483-493页

作者： Mahbub, Md Shahriar Wagner, Markus Crema, Luigi Fdn Bruno Kessler Via Sommarive 18 I-38123 Povo Trento Italy Univ Trento Via Sommarive 9 I-38123 Povo Trento Italy Univ Adelaide Adelaide SA 5005 Australia

Energy plays a key factor in the advancement of humanity. As energy demands are mostly met by fossil fuels, the world-wide consciousness grows about their negative impact on the environment. Therefore, it becomes necessary to design sustainable energy systems by introducing renewable energies. Because of the intermittent availability of different renewable resources, the designing of a sustainable energy system should find an optimal mix of different resources. However, the optimization of this combination has to deal with a number of possibly contradictory objectives. multi-objective evolutionary algorithms (MOEA) are widely used to solve this kind of problems. As optimizing an energy system by using a MOEA is computationally costly, it is necessary to solve the problem efficiently. For this purpose, we propose the incorporation of domain knowledge related to energy systems into different phases (i.e., initialization and mutation) of a MOEA run. The proposed approaches are implemented for two widely used MOEAs and evaluated on the Danish Aalborg test problem. The experimental results show that each approach individually achieves significant improvements of the energy systems, which is expressed in better trade-off sets. Moreover, a state-of-the-art stopping criterion is adapted to detect the convergence in order to save computational resources. Finally, all proposed techniques are merged within two MOEAs with the result that our combined approaches yield significantly better results in less time than generic approaches. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Domain knowledge Energy system optimization Initialization multi-objective evolutionary algorithm

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Remote Sensing Image Fusion Based on multi-objective evolutionary algorithm

Remote Sensing Image Fusion Based on Multi-objective Evoluti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhou, Xiuling Song, Mengxin Guo, Ping Yao, Li Beijing Normal Univ Lab Image Proc & Pattern Recognit Beijing 100875 Peoples R China Beijing City Univ Aftificial Intelligence Inst Beijing Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China

ISBN: (纸本)9781424465880

The purpose of fusion the multispectral (MS) and panchromatic (PAN) remote sensing images is to obtain high spatial resolution and quality of the PAN image as well as to preserve spectral information of the MS image. The parameter selection of fusion rule will directly affect the fusion result. In this paper, a new fusion method is presented based on multi-objective evolutionary algorithm (called SMS-EMOA). First, the MS image is converted from the RGB color space into the HSI (Hue-Saturation- Intensity) color space. Then, by applying Contourlet transform to the PAN image and the Intensity component of the MS image, the weighted model is used to fuse the sub-images, and the SMS-EMOA is adopted for optimal parameter selection. Finally, a fusion image is obtained by the inverse Contourlet and HSI transform. The experimental results show that the proposed fusion rule optimization method not only can gain the spatial resolution, but also can preserve the spectral information of the original MS image very well.

关键词： image fusion contourlet transform multi-objective evolutionary algorithm hypervolume

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