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decomposition-based evolutionary algorithm with automatic estimation to handle many-objective optimization problem

INFORMATION SCIENCES 2021年 546卷 1030-1046页

作者： Zhao, Chunliang Zhou, Yuren Chen, Zefeng Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Sun Yat Sen Univ Collaborat Innovat Ctr High Performance Comp Guangzhou 510006 Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

In many-objective optimization problems (MaOPs), the decomposition-based algorithms are widely used since they have promising performances in maintaining the diversity of solutions. However, few studies have been reported on how to utilize relationships between subproblems to promote global convergence. To fill this gap, we develop an automatic estimation mechanism based on the modified Ant Colony algorithm to assist the co evolution between subproblems, where two species of ants are designed. The working-ants execute local exploitation by recording the information of subproblems. The commandants control global exploration by adjusting co-evolution between working-ants. Moreover, the automatic estimation mechanism is expanded into three modes to verify the more efficient one, and they are embedded separately in the decomposition-based algorithm to construct the combined algorithms. The proposed algorithms are compared with five state-of-the-art algorithms on multiple test suites. The experimental results show that the proposed algorithms perform comparably or better than all referenced algorithms. Given the better performance of the proposed algorithms, it is evident that the hybrid mechanism may be a potential manner to handle MaOPs. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Many-objective optimization Automatic estimation Subproblem relationship decomposition-based algorithm

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Efficient decomposition-based algorithm to solve long-term pipeline scheduling problem

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Petroleum Science 2019年第5期16卷 1159-1175页

作者： S.Moradi S.A.Mir Hassani F.Hooshmand Department of Mathematics and Computer Science Amirkabir University of Technology(Tehran Polytechnic)TehranIran

This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution *** increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very *** this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable *** algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few *** idea behind the cut generation is based on the knowledge of the underlying problem *** experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.

关键词： Multi-product oil pipeline Batch sequencing decomposition-based algorithm Combinatorial cuts Heuristic method

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An Improvement Study of the decomposition-based algorithm Global WASF-GA for Evolutionary Multiobjective Optimization 1

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18th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

作者： Gonzalez-Gallardo, Sandra Saborido, Ruben Ruiz, Ana B. Luque, Mariano Univ Malaga Programa Doctorado Econ & Empresa C Ejido 6 E-29071 Malaga Spain Concordia Univ Dept Comp Sci & Software Engn 1455 Maisonneuve Blvd West Montreal PQ H3G 1M8 Canada Univ Malaga Dept Appl Econ Math C Ejido 6 E-29071 Malaga Spain

ISBN: (数字)9783030003746

ISBN: (纸本)9783030003746;9783030003739

The convergence and the diversity of the decomposition-based evolutionary algorithm Global WASF-GA (GWASF-GA) relies on a set of weight vectors that determine the search directions for new non-dominated solutions in the objective space. Although using weight vectors whose search directions are widely distributed may lead to a well-diversified approximation of the Pareto front (PF), this may not be enough to obtain a good approximation for complicated PFs (discontinuous, non-convex, etc.). Thus, we propose to dynamically adjust the weight vectors once GWASF-GA has been run for a certain number of generations. This adjustment is aimed at re-calculating some of the weight vectors, so that search directions pointing to overcrowded regions of the PF are redirected toward parts with a lack of solutions that may be hard to be approximated. We test different parameters settings of the dynamic adjustment in optimization problems with three, five, and six objectives, concluding that GWASF-GA performs better when adjusting the weight vectors dynamically than without applying the adjustment.

关键词： Evolutionary multiobjective optimization decomposition-based algorithm GWASF-GA Weight vector

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Data-driven stochastic robust optimization of sustainable utility system

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2023年 188卷

作者： Wang, Qipeng Zhao, Liang East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Engn Res Ctr Proc Syst Engn Minist Educ Shanghai 200237 Peoples R China

Sustainable utility systems reduce reliance on fossil fuels by using renewable energy sources. Multi-scale uncertainties associated with renewable energy and utility systems pose challenges to the modeling and optimization of sustainable utility systems. This study proposes a sustainable retrofit framework for utility systems based on a data-driven stochastic robust optimization approach. Kernel density estimation and fuzzy clustering were employed to capture the uncertainty features in a holistic framework. A life cycle assessment approach was used to calculate the global warming potential (GWP) of the sustainable utility system, and a multi-objective environmental and economic optimization model was developed. A nested decomposition-based algorithm was proposed to solve a large-scale mixed-integer nonlinear programming problem. Finally, a case study of an industrial utility system was conducted to demonstrate the effectiveness of the proposed method. The optimization results show that the proposed method reduces GWP by 9 % after introducing renewable energy and achieves a balance between economic and environmental performance.

关键词： Industrial utility system Renewable energy Multi-scale uncertainties Stochastic robust optimization decomposition-based algorithm Carbon reduction

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Benders decomposition-based particle swarm optimization for competitive supply networks with a sustainable multi-agent platform and virtual alliances

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APPLIED SOFT COMPUTING 2022年第0期114卷 107985-107985页

作者： Rezaei, S. Behnamian, J. Bu Ali Sina Univ Dept Ind Engn Hamadan Hamadan Iran

The involvement of competition in supply networks has changed the existing monopoly platform in different fields. This paper examines a multi-level decision-making framework within a triple-stage strategic approach in competitive supply networks. The various levels of these supply networks consist of parent firms (parent brands), manufacturing plants, state-owned logistics company and franchised sales centers. The parent brands, while following the strategies of the state logistics company (as the leader of the game), seek to further expand their market share in the production, supply and sales sectors. The main contributions of the proposed approach are: the existence of partnership and non-partnership synergies in different stages of planning, the emergence and development of supply networks based on downstream alliances, the design of a multi-agent distribution mechanism based on environmental sustainability requirements, and the simultaneous development of cooperation and competition in terms of virtual alliances. Further, given the features of the issue under discussion, a hybrid Benders decomposition-Particle Swarm Optimization algorithm is utilized. The designed structure of the algorithm helps to facilitate high-dimensional problem-solving while also addressing the interactive requirements of competitive games. The results of comparing the proposed solution approach with a game-theoretical heuristic, pure Benders decomposition and bi-level sub-population genetic algorithm prove its better performance, especially in large-size instances. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Competitive supply network decomposition-based algorithm Multi-agent distribution Sustainability Virtual alliance Franchised relationships

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Machine learning-based atom contribution method for the prediction of surface charge density profiles and solvent design

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AICHE JOURNAL 2021年第2期67卷 e17110-e17110页

作者： Liu, Qilei Zhang, Lei Tang, Kun Liu, Linlin Du, Jian Meng, Qingwei Gani, Rafiqul Dalian Univ Technol Sch Chem Engn Inst Chem Proc Syst Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Pharmaceut Sci & Technol State Key Lab Fine Chem Dalian Peoples R China Dalian Univ Technol Ningbo Inst Ningbo Peoples R China PSE SPEED Skyttemosen 6 Allerod Denmark Korea Adv Inst Sci & Technol KAIST Dept Chem & Biomol Engn Daejeon South Korea

Solvents are widely used in chemical processes. The use of efficient model-based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic, environment and human health properties. In this paper, an optimization-based MLAC-CAMD framework is established for solvent design, where a novel machine learning-based atom contribution method is developed to predict molecular surface charge density profiles (sigma-profiles). In this method, weighted atom-centered symmetry functions are associated with atomic sigma-profiles using a high-dimensional neural network model, successfully leading to a higher prediction accuracy in molecular sigma-profiles and better isomer identifications compared with group contribution methods. The new method is integrated with the computer-aided molecular design technique by formulating and solving a mixed-integer nonlinear programming model, where model complexities are managed with a decomposition-based strategy. Finally, two case studies involving crystallization and reaction are presented to highlight the wide applicability and effectiveness of the MLAC-CAMD framework.

关键词： atom contribution computer-aided molecular design decomposition-based algorithm machine learning surface charge density profiles (sigma-profiles)

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A decomposition approach for solving tri-level defender-attacker-defender problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 153卷 107085-107085页

作者： Ghorbani-Renani, Nafiseh Gonzalez, Andres D. Barker, Kash Univ Oklahoma Sch Ind & Syst Engn Norman OK 73019 USA

Network-based systems widely appear in different service, community, industrial, and economic systems such as electric power, water supply, transportation, and telecommunication networks. Due to the significant role of such systems in society, it is essential to have an effective plan to enhance the resilience of infrastructure networks against disruption (e.g., natural disasters, malevolent attacks, or operational failures). In relation to the concept of resilience, two relevant questions arise: (i) how does performance degrade after a disruption, or what is the vulnerability of the system? and (ii) how rapid does the disrupted system return to the desired performance level, or how can we characterize the system's recoverability? To enhance the resilience of a system against disruption, we address simultaneous actions of vulnerability reduction and recoverability enhancement through interdiction model, particularly defender-attacker-defender (DAD) model. However, the proposed model is computationally challenging to solve. To deal with this issue, we design a decomposition-based solution algorithm as a general framework to optimally solve tri-level DAD models in more efficiently. The proposed solution technique is demonstrated with the existing DAD model, namely a tri-level protection-interdiction-restoration model. To define the critical components subject to protection and disruption, an efficient clustering technique is applied which results in generating three sets of candidate components based on three centrality measures. We represent an illustrative case study based on the system of interdependent infrastructure networks in Shelby County, TN, for which we solve the model and assess the computational results for each set of candidate components. The results indicate that the proposed solution algorithm substantially outperforms the traditional covering decomposition method with regard to computational complexity, particularly for the higher budget scenarios. F

关键词： Resilience Interdependent infrastructure networks Network interdiction model Tri-level optimization decomposition-based algorithm

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Distributionally robust facility location with bimodal random demand

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COMPUTERS & OPERATIONS RESEARCH 2021年 134卷 105257-105257页

作者： Shehadeh, Karmel S. Sanci, Ece Lehigh Univ Dept Ind & Syst Engn Bethlehem PA 18015 USA Univ Bath Sch Management Bath Avon England

In this paper, we consider a decision-maker who wants to determine a subset of locations from a given set of candidate sites to open facilities and accordingly assign customer demand to these open facilities. Unlike classical facility location settings, we focus on a new setting where customer demand is bimodal, i.e., display, or belong to, two spatially distinct probability distributions. We assume that these two distributions are ambiguous, and only their mean values and ranges are known. Therefore, we construct a scenario-wise ambiguity set with two scenarios corresponding to the demand's two distinct distributions. Then, we formulate a distributionally robust facility location (DRFL) model that seeks to find the number and locations of facilities to open that minimize the fixed cost of opening facilities and the worst-case (maximum) expectation of transportation and unmet demand costs. We take the worst-case expectations over all possible demand distributions residing in the scenario-wise ambiguity set. We propose a decomposition-based algorithm to solve our min-max DRFL model and derive lower bound inequalities that accelerate the algorithm's convergence. In a series of numerical experiments, we demonstrate our approach's superior computational and operational performance compared with the stochastic programming approach and a distributionally robust approach that does not consider the demand's bimodality. Our results draw attention to the need to consider the multi-modality and ambiguity of the demand distribution in many strategic real-world problems.

关键词： Facility location Distributionally robust optimization Bimodal demand Mixed-integer programming decomposition-based algorithm

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Heuristics for integrated blending optimisation in a mining supply chain

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2021年 102卷 102373-102373页

作者： Haonan, Zhou Samavati, Mehran Hill, Andrew J. Univ Sydney Australian Ctr Field Robot Rio Tinto Ctr Mine Automat 8 Little Queen St Chippendale NSW Australia Univ Sheffield Sch Management Conduit Rd Sheffield S10 1FL S Yorkshire England

In a mining supply chain, products from mines are blended at port terminals to ensure that a set of blending targets (such as grade and qualities) are achieved. The production scheduling problem of each individual mine and the blending problem for a network of mines and ports constitute the integrated blending optimisation, which involves modelling of material flows from mine-side pits to port-side stock-piles. Due to the problem scale and the bilinear constraints for blending behaviours, the problem is com-putationally hard to solve by any available optimisers. This paper extends upon a decomposition-based algorithm in the literature, which was first to solve the blending problem for a network of multiple mines and ports over multiple time periods. In our paper, a prune routine is proposed to progressively update the mixed integer program of the production scheduling problem for each mine during a rolling-horizon heuristic. Experiments have shown that this extension produces solutions of higher quality than the orig-inal algorithm. Furthermore, a ranking-based topological sorting heuristic is presented for selecting units of mineral deposits, known as 'blocks'. Experiments have shown that the average computation time can be reduced by 75.97% when this heuristic is implemented. On top of these extensions, an adaptive algo-rithm is adopted from the decomposition-based algorithm, featuring faster convergence and higher solu-tion quality at the same time. Comparing our results to the literature, our adaptive algorithm, on average, yields an improvement in solution quality by 12.67% while reducing computation time by 65.09%. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Scheduling Integrated blending optimisation Open-pit mine optimisation decomposition-based algorithm Topological sorting

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An evolutionary decomposition-based multi-objective feature selection for multi-label classification

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PEERJ COMPUTER SCIENCE 2020年第3期6卷 e261页

作者： Bidgoli, Azam Asilian Ebrahimpour-Komleh, Hossein Rahnamayan, Shahryar Univ Kashan Dept Elect & Comp Engn Kashan Iran Ontario Tech Univ Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many recent real-world applications in which each entity has more than one label. To improve the performance of multi-label classification, feature selection plays an important role. It involves identifying and removing irrelevant and redundant features that unnecessarily increase the dimensions of the search space for the classification problems. However, classification may fail with an extreme decrease in the number of relevant features. Thus, minimizing the number of features and maximizing the classification accuracy are two desirable but conflicting objectives in multi-label feature selection. In this article, we introduce a multi-objective optimization algorithm customized for selecting the features of multi-label data. The proposed algorithm is an enhanced variant of a decomposition-based multi-objective optimization approach, in which the multi-label feature selection problem is divided into single-objective subproblems that can be simultaneously solved using an evolutionary algorithm. This approach leads to accelerating the optimization process and finding more diverse feature subsets. The proposed method benefits from a local search operator to find better solutions for each subproblem. We also define a pool of genetic operators to generate new feature subsets based on old generation. To evaluate the performance of the proposed algorithm, we compare it with two other multi-objective feature selection approaches on eight real-world benchmark datasets that are commonly used for multi-label classification. The reported results of multi-objective method evaluation measures, such as hypervolume indicator and set coverage, illustrate an improvement in the results obta

关键词： Feature selection Multi-label classification Multi-objective optimization decomposition-based algorithm Evolutionary algorithm

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