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A multi-objective optimization model and its evolution-based solutions for the fingertip localization problem

PATTERN RECOGNITION 2018年 74卷 385-405页

作者： Gong, Dunwei Liu, Ke China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

Exact fingertip positions are of particular importance to the fingertip-based human-computer interaction. We build a multi-objective optimization model for the problem of fingertip localization, and present a method to solve the above model based on evolutionary algorithms. When building the model, we take the positions of a series of pixels as the decision variable, the shape of the hand-edge curve corresponding to each of the pixels as one objective function, and the distance between each of the pixels and the gravity center of the palm as the other objective function. In addition, based on the correlation among the positions of pixels of the fingertip regions, we present a multi-objective estimation of distribution algorithm to solve the model so as to obtain the best pixel set, thus gaining the fingertip positions. The experimental results demonstrate the effectiveness of the proposed model and algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Fingertip localization Multi-objective optimization estimation of distribution algorithm Probability distribution model

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A Bayesian Network Approach to Program Generation

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2008年第6期12卷 750-764页

作者： Hasegawa, Yoshihiko Iba, Hitoshi Univ Tokyo Grad Sch Frontier Sci Dept Frontier Informat Chiba 2778561 Japan

Genetic programming (GP) is a powerful optimization algorithm that has been applied to a variety of problems. This algorithm can, however, suffer from problems arising from the fact that a crossover, which is a main genetic operator in GP, randomly selects crossover points, and so building blocks may be destroyed by the action of this operator. In recent years, evolutionary algorithms based on probabilistic techniques have been proposed in order to overcome this problem. In the present study, we propose a new program evolution algorithm employing a Bayesian network for generating new individuals. It employs a special chromosome called the expanded parse tree, which significantly reduces the size of the conditional probability table (CPT). Prior prototype tree-based approaches have been faced with the problem of huge CPTs, which not only require significant memory resources, but also many samples in order to construct the Bayesian network. By applying the present approach to three distinct computational experiments, the effectiveness of this new approach for dealing with deceptive problems is demonstrated.

关键词： Bayesian network estimation of distribution algorithm genetic programming (GP)

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A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop

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KNOWLEDGE-BASED SYSTEMS 2020年 194卷 105536-105536页

作者： Zheng, Jie Wang, Ling Wang, Jing-jing Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

With consideration of uncertainty in the distributed manufacturing systems, this paper addresses a multi-objective fuzzy distributed hybrid flow shop scheduling problem with fuzzy processing times and fuzzy due dates. To optimize the fuzzy total tardiness and robustness simultaneously, a cooperative coevolution algorithm with problem-specific strategies is proposed by reasonably combining the estimation of distribution algorithm (EDA) and the iterated greedy (IG) search. In the EDA-mode search, a problem-specific probability model is established to reduce the solution space and a sample mechanism is proposed to generate new individuals. To enhance exploitation, a specific local search is designed to improve performance of non-dominated solutions. Moreover, destruction and reconstruction methods in the IG-mode search are employed for further exploiting better solutions. To balance exploration and exploitation capabilities, a cooperation scheme for mode switching is designed based on the information entropy and the diversity of elite solutions. The effect of the key parameters on the performances of the proposed algorithm is investigated by Taguchi design of experiment method. Comparative results and statistical analysis demonstrate the effectiveness of the proposed algorithm in solving the problem. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-objective fuzzy distributed hybrid flow shop Fuzzy processing times and due dates Robustness Cooperative coevolution algorithm estimation of distribution algorithm Iterated greedy search

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Clustering and learning Gaussian distribution for continuous optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 2005年第2期35卷 195-204页

作者： Lu, Q Yao, X Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

Since the estimation of distribution algorithm (EDA) was introduced, different approaches in continuous domains have been developed. Initially, the single Gaussian distribution was broadly used when building the probabilistic models, which would normally mislead the search when dealing with multimodal functions. Some researchers later constructed EDAs that take advantage of mixture probability distributions by using clustering techniques. But their algorithms all need prior knowledge before applying clustering, which is unreasonable in real life. In this paper, two new EDAs for continuous optimization are proposed, both of which, incorporate clustering techniques into estimation process to break the single Gaussian distribution assumption.. The new algorithms, Clustering and estimation of Gaussian Network algorithm based on BGe metric and Clustering and estimation of Gaussian distribution algorithm, not only show great advantage in optimizing multimodal functions with a few local optima, but also overcome the restriction of demanding prior knowledge before clustering by using a very reliable clustering technique, Rival Penalized Competitive Learning. This is the first time that EDAs have the ability to detect the number of global optima automatically. A set of experiments have been implemented to evaluate the performance of new algorithms. Besides the improvement over some multimodal functions, according to the No Free Lunch theory, their weak side is also showed.

关键词： clustering estimation of distribution algorithm Gaussian distribution

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Determining the inspection intervals for one-shot systems with support equipment

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2018年 169卷 63-75页

作者： Zhao, Qian Qian Yun, Won Young Pusan Natl Univ Dept Ind Engn 30 Jangjeon Dong Busan 609735 South Korea

This paper considers systems that comprise one-shot devices and support equipment. One-shot devices are stored for long periods of time, and failures are detected only upon inspection. The support equipment needed to operate one-shot devices is maintained immediately upon failure. This paper addresses the inspection schedule problem for such systems with limited maintenance resources. The interval availability and life cycle cost are used as optimization criteria. The aim is to determine near-optimal inspection intervals for one-shot systems to minimize the expected life cycle cost and satisfy the target interval availability between inspection periods. An estimation of distribution algorithm (EDA) and a heuristic method are proposed to find the near-optimal solutions, and numerical examples are given to demonstrate the effects of the various model parameters to the near-optimal inspection intervals. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： One-shot device Support equipment Interval availability estimation of distribution algorithm

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A New Evolutionary algorithm with Structure Mutation for the Maximum Balanced Biclique Problem

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第5期45卷 1040-1053页

作者： Yuan, Bo Li, Bin Chen, Huanhuan Yao, Xin Univ Sci & Technol China USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230027 Peoples R China Univ Birmingham Sch Comp Sci Ctr Excellence Res Computat Intelligence & Applic Birmingham B15 2TT W Midlands England Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

The maximum balanced biclique problem (MBBP), an NP-hard combinatorial optimization problem, has been attracting more attention in recent years. Existing node-deletion-based algorithms usually fail to find high-quality solutions due to their easy stagnation in local optima, especially when the scale of the problem grows large. In this paper, a new algorithm for the MBBP, evolutionary algorithm with structure mutation (EA/SM), is proposed. In the EA/SM framework, local search complemented with a repair-assisted restart process is adopted. A new mutation operator, SM, is proposed to enhance the exploration during the local search process. The SM can change the structure of solutions dynamically while keeping their size (fitness) and the feasibility unchanged. It implements a kind of large mutation in the structure space of MBBP to help the algorithm escape from local optima. An MBBP-specific local search operator is designed to improve the quality of solutions efficiently;besides, a new repair-assisted restart process is introduced, in which the Marchiori's heuristic repair is modified to repair every new solution reinitialized by an estimation of distribution algorithm (EDA)-like process. The proposed algorithm is evaluated on a large set of benchmark graphs with various scales and densities. Experimental results show that: 1) EA/SM produces significantly better results than the state-of-the-art heuristic algorithms;2) it also outperforms a repair-based EDA and a repair-based genetic algorithm on all benchmark graphs;and 3) the advantages of EA/SM are mainly due to the introduction of the new SM operator and the new repair-assisted restart process.

关键词： estimation of distribution algorithm evolutionary algorithms local search maximum balanced biclique problem (MBBP) structure mutation

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A hybrid multi-population framework for dynamic environments combining online and offline learning

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SOFT COMPUTING 2013年第12期17卷 2327-2348页

作者： Uludag, Gonul Kiraz, Berna Etaner-Uyar, A. Sima Ozcan, Ender Istanbul Tech Univ Inst Sci & Technol TR-34469 Istanbul Turkey Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

Population based incremental learning algorithms and selection hyper-heuristics are highly adaptive methods which can handle different types of dynamism that may occur while a given problem is being solved. In this study, we present an approach based on a multi-population framework hybridizing these methods to solve dynamic environment problems. A key feature of the hybrid approach is the utilization of offline and online learning methods at successive stages. The performance of our approach along with the influence of different heuristic selection methods used within the selection hyper-heuristic is investigated over a range of dynamic environments produced by a well known benchmark generator as well as a real world problem, referred to as the Unit Commitment Problem. The empirical results show that the proposed approach using a particular hyper-heuristic outperforms some of the best known approaches in literature on the dynamic environment problems dealt with.

关键词： Heuristic Metaheuristic Hyper-heuristic estimation of distribution algorithm Dynamic environment

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Advanced design and optimization of single mode photonic crystal fibers

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JOURNAL OF MODERN OPTICS 2009年第14期56卷 1572-1581页

作者： Pourmahayabadi, M. Nejad, S. Mohammad Iran Univ Sci & Technol Nanoptron Res Lab Dept Elect Engn Tehran Iran

This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In order to determine the properties of PCFs such as dispersion, dispersion slope and loss, an artificial intelligence method, the Nero-Fuzzy system, is applied. In addition, a special cost function which simultaneously includes the confinement loss, dispersion and its slope is used in the proposed design approach. The results revealed that the proposed method is a powerful tool for solving this optimization problem. The optimized PCF exhibits an ultra low confinement loss and low dispersion at 1.55 mu m wavelength with a nearly zero dispersion slope over the C communication band.

关键词： differential evolution estimation of distribution algorithm dispersion dispersion slope loss Nero-Fuzzy system photonic crystal fiber

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Sensibility of Linkage Information and Effectiveness of Estimated distributions

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EVOLUTIONARY COMPUTATION 2010年第4期18卷 547-579页

作者： Chuang, Chung-Yao Chen, Ying-ping Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

The probabilistic model building performed by estimation of distribution algorithms (EDAs) enables these methods to use advanced techniques of statistics and machine learning for automatic discovery of problem structures. However, in some situations, it may not be possible to completely and accurately identify the whole problem structure by probabilistic modeling due to certain inherent properties of the given problem. In this work, we illustrate one possible cause of such situations with problems consisting of structures with unequal fitness contributions. Based on the illustrative example, we introduce a notion that the estimated probabilistic models should be inspected to reveal the effective search directions and further propose a general approach which utilizes a reserved set of solutions to examine the built model for likely inaccurate fragments. Furthermore, the proposed approach is implemented on the extended compact genetic algorithm (ECGA) and experiments are performed on several sets of additively separable problems with different scaling setups. The results indicate that the proposed method can significantly assist ECGA to handle problems comprising structures of disparate fitness contributions and therefore may potentially help EDAs in general to overcome those situations in which the entire problem structure cannot be recognized properly due to the temporal delay of emergence of some promising partial solutions.

关键词： Sensible linkage effective distribution linkage sensibility probabilistic model model pruning estimation of distribution algorithm extended compact genetic algorithm evolutionary computation

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Hierarchical Energy Management for Power-Split Plug-In HEVs Using Distance-Based Optimized Speed and SOC Profiles

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第10期67卷 9312-9323页

作者： Lim, Hansang Su, Wencong Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea Univ Michigan Dept Elect & Comp Engn Dearborn MI 48128 USA

This paper proposes a distance-based two-stage energy management strategy for power-split plug-in hybrid electric vehicles (PHEVs). One stage is for long-term optimization and the other is for short-term adaptation to actual traffic conditions. Energy consumption in PHEVs depends on the characteristics of the drivetrain as well as the operating conditions such as power demands and their split. Thus, prior to departure, the operating conditions for a whole trip are optimized for the drivetrain characteristics and trip information, which generates optimal speed and state-of-charge profiles. While driving, the operating conditions are adapted to current traffic conditions for a short horizon on the basis of long-term optimization results. In consideration of the changeability of traffic conditions, the proposed energy management strategy is performed in a distance domain, which localizes the effects of changes in traffic conditions on the long-term optimization results. Therefore, this distance-based two-stage strategy improves the balance between the optimality and the real-time computing time, which is suitable for online management. A model for the propulsion system in a PHEV and the energy management strategy were formulated in a distance domain. An estimation of distribution algorithm was used for long-term optimization and local adaptation.

关键词： Distance-based long-term optimization local adaptation hierarchical scheme estimation of distribution algorithm

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