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Combining mutual information and stable matching strategy for dynamic evolutionary multi-objective optimization

ENGINEERING OPTIMIZATION 2018年第9期50卷 1434-1452页

作者： Fu, Xiaogang Sun, Jianyong Shanghai Dianji Univ Sch Elect Engn Shanghai Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China

It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence structure to facilitate population re-initialization in evolutionary search paradigms. The aim of the learning techniques is to discover knowledge from history information, thereby to track the movement of the optimal front quickly through good initialization when a change occurs. In this article, a new learning strategy is proposed, where the main ideas are (1) to use mutual information to identify the relationship between previously found approximated solutions;(2) to use a stable matching mechanism strategy to associate previously found optimal solutions bijectively;and (3) to re-initialize the new population based on a kinematics model. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.

关键词： multi-objective evolutionary algorithm dynamic multiobjective optimization kinematics model mutual information stable matching strategy

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AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction

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KNOWLEDGE-BASED SYSTEMS 2018年 146卷 58-72页

作者： Song, Shuangbao Gao, Shangce Chen, Xingqian Jia, Dongbao Qian, Xiaoxiao Todo, Yuki Univ Toyama Fac Engn Toyama 9308555 Japan Kanazawa Univ Fac Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

Despite half-century's unremitting efforts, the prediction of protein structure from its amino acid sequence remains a grand challenge in computational biology and bioinformatics. Two key factors are crucial to solving the protein structure prediction (PSP) problem: an effective energy function and an efficient conformation search strategy. In this study, we model the PSP as a multi-objective optimization problem. A three-objective evolution algorithm called AIMOES is proposed. AIMOES adopts three physical energy terms: bond energy, non-bond energy, and solvent accessible surface area. In AIMOES, an evolution scheme which flexibly reuse past search experiences is incorporated to enhance the efficiency of conformation search. A decision maker based on the hierarchical clustering is carried out to select representative solutions. A set of benchmark proteins with 30-91 residues is tested to verify the performance of the proposed method. Experimental results show the effectiveness of AIMOES in terms of the root mean square deviation (RMSD) metric, the distribution diversity of the obtained Pareto front and the success rate of mutation operators. The superiority of AIMOES is demonstrated by the performance comparison with other five state-of-the-art PSP methods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Reuse search experience Hierarchical clustering Protein structure prediction

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An evolutionary developmental approach for generation of 3D neuronal morphologies using gene regulatory networks

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NEUROCOMPUTING 2018年 273卷 346-356页

作者： Lin, Xianghong Li, Zhiqiang Ma, Huifang Wang, Xiangwen Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Gansu Peoples R China Wuhan Univ Coll Comp State Key Lab Software Engn Wuhan 430072 Hubei Peoples R China

Computational modeling of neuronal morphologies is significant for understanding structure-function relationships and brain information processing in computational neuroscience. Using a gene regulatory network model, an evolutionary developmental approach is presented for efficient generation of 3D virtual neurons. This approach describes the developmental process of dendritic morphologies by locally inter-correlating morphological variables which can be represented by the dynamics of gene expression. Then, the multi-objective evolutionary algorithm with gene segmental duplication and divergence operators is applied to evolve the virtual neurons, which aims at generating virtual neurons that are as good as the experimentally traced real neurons in terms of statistical morphological measurements. We experimentally generated motoneurons and statistically compared between the real neurons and the generated virtual neurons by measuring a series of emergent morphological features. The results show that the generated virtual neurons are seemingly realistic, accurate, and further suggest that this approach is an efficient tool for understanding neural development and investigating the relation of neuronal structure to function in particular. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Virtual neuron Neuronal morphology Gene regulatory network Artificial genome multi-objective evolutionary algorithm

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Stochastic Dual-objective Disassembly Sequence Planning with Consideration of Learning Effect 16

Stochastic Dual-objective Disassembly Sequence Planning with...

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16th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Guo, XiWang Zhou, MengChu Fu, YaPing Qi, Liang You, Dan Liaoning Shihua Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Qingdao Univ Inst Complex Sci Qingdao 266071 Shandong Peoples R China Shandong Univ Sci & Technol Dept Intelligent Sci & Technol Qingdao 266590 Shandong Peoples R China Zhejiang Gongshang Univ Dept Informat & Elect Engn Hangzhou 310008 Zhejiang Peoples R China

ISBN: (纸本)9781728100845

In an actual remanufacturing process, a human operator is able to continuously learn the disassembly knowledge of an end-of-life product when he/she disassembles it, which makes him/her disassemble it more proficiently. In order to describe this feature, this work proposes a stochastic dual objective disassembly sequencing planning problem considering human learning effects. In this problem, actual disassembly and setup time of operations are a function of their normal time and starting time. A new mathematical model is established to maximize total disassembly profit and minimize disassembly time. In order to solve this problem efficiently, a multi-population multi-objective evolutionary algorithm is developed. In this algorithm, some special strategies, e.g., solution representation, crossover operator and mutation operator, are newly designed based on this problem's characteristics. Its effectiveness is well illustrated through several numerical cases and by comparing it with two prior approaches, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. Experimental results demonstrate that the proposed algorithm performs well in solving this problem.

关键词： remanufacture disassembly sequencing planning problem learning effect multi-population multi-objective evolutionary algorithm stochastic dual-objective

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Enhanced θ dominance and density selection based evolutionary algorithm for many-objective optimization problems

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APPLIED INTELLIGENCE 2018年第4期48卷 992-1012页

作者： Zhou, Chong Dai, Guangming Wang, Maocai China Univ Geosci Sch Comp Wuhan 430074 Hubei Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Hubei Peoples R China

Many multi-objective evolutionary algorithms (MOEAs) have been developed for many-objective optimization. This paper proposes a new enhanced oee integral dominance and density selection based evolutionary algorithm (called oee integral-EDEA) for many-objective optimization problems. We firstly construct an m-dimension hyper-plane using the extreme point on the each dimension. Then we replace the distance between the origin point and projection of solution on the reference line of oee integral dominance which recently is proposed in oee integral dominance based evolutionary algorithm (oee integral-DEA), with the perpendicular distance between each solution and the hyper-plane to develop an enhanced oee integral dominance. Finally, in order to maintain better diversity, oee integral-EDEA employs density based selection mechanism to select the solution for the next population in the environment selection phase. oee integral-EDEA still inherits clustering operator and ranking operator of oee integral-DEA to balance diversity and convergence. The performance of oee integral-EDEA is validated and compared with five state-of-the-art algorithms on two well-known many-objective benchmark problems with three to fifteen objectives. The results show that oee integral-EDEA is capable of obtaining a solution set with better convergence and diversity.

关键词： Many-objective Optimization multi-objective evolutionary algorithm theta dominance m-dimension hyper-plane Density

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PGA/MOEAD: a preference-guided evolutionary algorithm formulti-objective decision-making problems with interval-valued fuzzy preferences

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第3期49卷 595-616页

作者： Luo, Bin Lin, Lin Zhong, ShiSheng Harbin Inst Technol Sch Mechatron Engn Harbin Heilongjiang Peoples R China

In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.

关键词： multi-criteria decision-making Interval-valued fuzzy preferences Boundary intersection multi-objective evolutionary algorithm based on decomposition evolutionary Computation multi-objective optimisation

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Selection methods and diversity preservation in many-objective evolutionary algorithms

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DATA TECHNOLOGIES AND APPLICATIONS 2018年第4期52卷 502-519页

作者： Marti, Luis Segredo, Eduardo Sanchez-Pi, Nayat Hart, Emma Univ Fed Fluminense Inst Comp Niteroi RJ Brazil Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland Univ La Laguna Dept Ingn Informat & Sistemas San Cristobal De La Lagu Spain Univ Estado Rio de Janeiro Rio De Janeiro Brazil

Purpose - One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach - Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings - The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic algorithm III and a modified version of the Non-dominated Sorting Genetic algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value - The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

关键词： Convergence Diversity preservation Many-objective optimisation multi-objective evolutionary algorithm Selection mechanism Walking Fish Group (WFG) test suite

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Cost-effective Network Planning for Rural Communities 2

Cost-effective Network Planning for Rural Communities

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International Conference on Computing, Electronics and Communications Engineering (iCCECE)

作者： Islam, Nazrul Phillips, Chris Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9781728121383

Broadband Internet access is central to the regeneration of remote communities and reducing the digital divide between rural and urban regions. This paper focuses on rural communities with limited financial resources, environmental issues including long reach from conurbations, and mountainous or otherwise adverse terrain, typically with limited access to a wired power supply. As such, regular access technologies based on cable or fibre optics are not financially viable. To overcome this challenge, we consider the deployment of a Free-Space Optical (FSO) based relay network as primary technology, using diversity to provide resilience to atmospheric effects. More precisely, this paper describes a novel network planning tool based on a multi-objective evolutionary algorithm (MOEA), that determines the suitable location of FSO relay nodes, taking into account end-to-end link speed and the degree of path diversity. This MOEA approach can account for Line-of-Sight occlusions and allows various compromises to be selected from a Pareto front to suit individual needs. We provide suitable results to show the satisfactory operation of the tool and outline avenues for future development.

关键词： Free-Space Optical Network Planning multi-objective evolutionary algorithm

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multi-objective and multi-Criteria Optimization of Microgrids for Nearly Zero-Energy Buildings 2

Multi-Objective and Multi-Criteria Optimization of Microgrid...

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2nd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Galisai, Simone Ghiani, Emilio Pilo, Fabrizio Univ Cagliari Dept Elect & Elect Engn Cagliari Italy

ISBN: (纸本)9781728111568

Nearly zero-energy buildings (NZEBs) are high energy performance buildings in which part of the amount of energy that these buildings require comes mostly from renewable sources. In order to obtain the target of nearly zero energy consumption, the electrical loads and the energy sources related to the NZEB building can be organized as a Microgrid, which needs to be optimally sized in his components. The optimal sizing of a Microgrid for NZEBs can be formulated as multi-objective problem. In fact, for the NZEB owner exists an economic target aimed at maximizing profits from micro generation, a different one aimed solely to minimize the energy bought from the market and an environmental target aimed at minimizing the global CO2 emissions. These objectives can enter into conflict and create the need for combined optimization. In this paper, this optimization problem is investigated with an integrated framework addressing the multi-objective optimization and multi-criteria evaluation issues simultaneously Minimize the investment cost, maximize the fraction of energy self-consumed with renewable energy sources and reduce the CO2 emissions will be considered as three objectives for multi-objective optimization. The proposed methodology is applied to a microgrid for a NZEB public building. The simulation results show the effectiveness of the proposed methodology.

关键词： Microgrid NZEBs Renewable sources multi-objective evolutionary algorithm Real-time pricing

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multi-objective Flexible Job Shop Scheduling Problem Based on Non-Dominated Genetic algorithm

Multi-Objective Flexible Job Shop Scheduling Problem Based o...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Yang Yacong Wang Aimin Ge Yan School of Mechanical Engineering Department Institute of Technology Beijing China

ISBN: (数字)9781728182889

ISBN: (纸本)9781728182896

Aiming at the limitation of flexible job shop scheduling (JSP), a multi-objective optimization model of JSP is constructed, which takes the maximum completion time and delivery time as the objective function. Considering the actual inventory size of job shop, a non dominated genetic algorithm is proposed. Combined with the actual inventory and demand, the program is developed to verify the feasibility and effectiveness of the algorithm. The results show that the algorithm can meet the delivery time and the maximum completion time at the same time, considering the size of inventory and achieve satisfactory results.

关键词： component Flexible job shop scheduling multi-objective evolutionary algorithm Non dominated sorting genetic algorithm Job shop scheduling multi-objective evolutionary algorithm Completion time delivery date JSP Genetic algorithm (GA) inventories FLEXIBLE

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