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17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP) / 14th International Conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Qin, Bin Tseng, Shih-Pang Changzhou Inst Ind Technol Modern Equipment Mfg Coll Changzhou Jiangsu Peoples R China Changzhou Coll Informat Technol Sch Software & Big Data 22 Mingxin Middle Rd Changzhou 213164 Jiangsu Peoples R China

ISBN: (纸本)9789811910531;9789811910524

Puzzle is a game to have a very long history for training the human logic thinking. In addition, the puzzle-solving methods can be used in various practical applications. In this paper, we proposed an EDA-based edge-matching puzzle solver. The proposed approach is based on the probability model. We have presented how to build the suitable probability model for puzzle solving. And we also provide the sampling method to construct a candidate solution. The experimental results show the proposed approach is available and effective.

关键词： estimation of distribution algorithm Meta-heuristics Puzzle solving

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Optimal model parameter estimation of solar and fuel cells using improved estimation of distribution algorithm

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1693-1700页

作者： Chandran, Benin Pratap Selvakumar, A. Immanuel Let, G. Shine Sathiyan, S. Paul Karunya Inst Technol & Sci Dept EEE Coimbatore 641114 Tamil Nadu India Karunya Inst Technol & Sci Dept ECE Coimbatore 641114 Tamil Nadu India

Renewable energy through the use of fuel cells and solar cells is one of the popular developments in recent days that produce electricity. Accurate modelling of fuel cell and solar cells are essential in simulation and analysis of energy systems with these sources. However, the systems are extremely nonlinear and complicated. The model needs to be optimized under distinct operating circumstances. Enhanced and streamlined Improved estimation of distribution (IED) algorithm is suggested in this paper to estimate the parameter through optimization for solar cell models and fuel cell models. This is accomplished through the introduction of an ideal approach to improve population quality and the use of a local search to improve the efficiency of the finest global solution further. The design of an IED algorithm is much more straightforward and search efficiency is greatly improved compared with the fundamental optimization techniques from the literature. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Solar cell Fuel cell estimation of distribution algorithm Optimization Parameter estimation

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Handling Uncertainty in Financial Decision Making: A Clustering estimation of distribution algorithm With Simplified Simulation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2021年第1期5卷 42-56页

作者： Shi, Wen Chen, Wei-Neng Gu, Tianlong Jin, Hu Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Hanyang Univ Div Elect Engn Ansan 15588 South Korea

In financial decision making models, parameters are usually obtained based on historical data, which involve strong uncertainties. In some cases, the fluctuation caused by environmental uncertainty may even be more significant than that caused by utilizing different strategies. Such phenomenon makes the optimization and uncertainty handling in finical optimization a great challenge. In this article, a group insurance portfolio problem is considered as an instance of financial optimization with strong uncertainty. To handle uncertainty, we first analyze the feature of the problem and discover that in such kind of optimization problem with strong uncertainty, the solutions are strongly relative to the scenario. In view of the scenario-relevant feature, a simplified simulation approach is designed. Only one scenario is simulated for each generation in the evolution process to deal with the uncertainties. Combining this approach with a clustering estimation of distribution algorithm, a new algorithm (CEDA-SS) is proposed. estimation of current profit is made by Monte Carlo (MC) simulation based on historical data. Solutions in each generation are evaluated in the same scenario. Two kinds of clustering mechanisms are applied to further improve the performance of the algorithm. Moreover, a comparison mechanism based on the Wilcoxon rank sum test is proposed to evaluate the performance of the algorithms. Experimental results show that the proposed CEDA-SS is suitable for the group insurance portfolio problem and it outperforms other uncertain evolutionary algorithms.

关键词： Uncertainty Decision making Optimization Insurance Portfolios Clustering algorithms Computational modeling Financial decision making optimization under uncertainty insurance portfolio estimation of distribution algorithm

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A hybrid estimation of distribution algorithm for solving assembly flexible job shop scheduling in a distributed environment

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Du, Baigang Han, Shuai Guo, Jun Li, Yibing Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China

This paper proposes a novel distributed assembly flexible job shop scheduling problem (DAFJSP), which involves three stages: production stage, assembly stage, and delivery stage. The production stage is accomplished in a few flexible job shops, the assembly stage is accomplished in a few single -machine factories, and the delivery stage is to deliver the obtained products to the corresponding customers. To address the problem, a hybrid estimation of distribution algorithm based on differential evolution operator and variable neighborhood search (HEDA-DEV) is proposed with the goal of minimizing the total cost and tardiness. Firstly, a new multidimensional coding method is designed based on the features of the DAFJSP. Secondly, two mutation operators and the similarity coefficient based on the probability matrix are put forward to implement the dynamic mutation. Thirdly, five types of neighborhood structures satisfying cooperative search strategies are employed to adequately improve the local exploitation ability. Finally, the comparison experiment results suggest that the proposed HEDA-DEV has competitive performance compared to the selected efficient algorithms. Moreover, a real case study is used to demonstrate that HEDA-DEV is an effective method for solving DAFJSP.

关键词： Distributed assembly flexible job shop scheduling problem estimation of distribution algorithm Differential evolution Variable neighborhood search

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An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Zhang, Zi-Qi Xu, Yan-Xuan Qian, Bin Hu, Rong Wu, Fang-Chun Wang, Ling Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

With the trend of economic globalization, distributed manufacturing widely exists in modern manufacturing systems. As an extension of the distributed flowshop scheduling problem, the distributed no -wait flowshop group scheduling problem with sequence -dependent setup times (DNFGSP_SDSTs) is investigated in this article. To address DNFGSP_SDSTs with the criterion of minimizing makespan, this study proposes an enhanced estimation of distribution algorithm & sdot;(EEDA) with problem -specific knowledge. First, a mixed integer linear programming (MILP) model of DNFGSP_SDSTs is established. Second, based on the characteristics of DNFGSP_SDSTs, five problem -specific properties about local search operators are derived as prior knowledge to reduce computational cost. Third, two NEH-based two -stage heuristics are presented to construct a high -quality population with diversity. Fourth, a probability model with problem -specific knowledge and a family -based updating mechanism are developed to accumulate valuable pattern information from high -quality solutions, while a sampling strategy is designed to generate new populations with the accumulated information. Fifth, several local search operators are devised to refine the obtained solutions. Furthermore, perturbation and reinitialization methods are developed to avoid premature convergence. Finally, the validity of the MILP model is verified by using the Gurobi solver. The parameters of EEDA are tuned through a design of experiments. The effectiveness of key components in EEDA is confirmed through extensive experiments, and the computational comparisons with the state-of-theart algorithms indicate the effectiveness of the proposed EEDA for solving DNFGSP_SDSTs.

关键词： Flowshop group scheduling Distributed no-wait flowshop group scheduling estimation of distribution algorithm Problem-specific knowledge

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An enhanced Kalman filtering and historical learning mechanism driven estimation of distribution algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Zhu, Ningning Zhao, Fuqing Wang, Ling Dong, Chenxin Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Qingdao Hengxing Univ Sci & Technol Sch Mech & Automot Engn Qingdao 266100 Peoples R China

As a representative evolutionary algorithm based on probabilistic models, the estimation of distribution algorithm (EDA) is widely applied in complex continuous optimization problems based on remarkable characteristics of modeling with macro-dominant information. However, the success of EDA depends on the quality of dominant solutions, modeling, sampling methods, and the efficiency of searching. An enhanced Kalman filtering and historical learning mechanism-driven EDA (KFHLEDA) is proposed to adjust the search direction and enlarge the search range of classical EDA in this paper. The enhanced Kalman filtering is designed in allusion to specific problems during the search through the prediction, observation, and the first and second revision stages. A historical archive is integrated into KFHLEDA to store the elite individuals with specific knowledge and diverse solutions from Kalman filtering. The elite strategy is embedded in the revision improvement matrix to revise modeling data, which is fed back to the probabilistic model through the historical learning mechanism with previous promising solutions to estimate the covariance matrix. The population adaptive adjustment strategy is introduced to reduce the number of invalid iterations. The effectiveness of the proposed KFHLEDA is proved through theoretical analysis. The evaluation results on benchmark functions of the CEC 2017 test suit validate that the KFHLEDA is efficient and competitive compared with fifteen classical metaheuristic algorithms and state-of-the-art EDA variants.

关键词： estimation of distribution algorithm Kalman filtering Historical learning mechanism Adaptive adjustment strategy Elite strategy

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An improved estimation of distribution algorithm for Solving Constrained Mixed-Integer Nonlinear Programming Problems

An improved Estimation of Distribution Algorithm for Solving...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Molina Perez, Daniel Alfredo Portilla-Flores, Edgar Mezura-Montes, Efren Vega-Alvarado, Eduardo Inst Politecn Nacl CIDETEC Ciudad De Mexico Mexico Inst Politecn Nacl UPIIT Tlaxcala Mexico Univ Veracruz Artificial Intelligence Res Ctr Veracruz Veracruz Mexico

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

In a mixed-integer nonlinear programming problem, integer restrictions divide the feasible region into discontinuous feasible parts with different sizes. Evolutionary algorithms (EAs) are usually vulnerable to being trapped in larger discontinuous feasible parts. In this work, an improved version of an estimation of distribution algorithm (EDA) is developed, where two new operations are proposed. The first one establishes a link between the learning-based histogram model and the s-constrained method. Here, the constraint violation level of the s-constrained method is used to explore the smaller discontinuous parts and form a better statistical model. The second operation is the hybridization of the EDA with a mutation operator to generate offspring from both the global distribution information and the parent information. A benchmark is used to test the performance of the improved proposal. The results indicated that the proposed approach shows a better performance against other tested EAs. This new proposal solves to a great extent the influence of the larger discontinuous feasible parts, and improve the local refinement of the real variables.

关键词： estimation of distribution algorithm evolutionary algorithms integer restriction handling mixed integer nonlinear programming

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NMIEDA: estimation of distribution algorithm based on normalized mutual information

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第6期33卷 e6074-e6074页

作者： Lin, Zhiyi Su, Qing Xie, Guobo Guangdong Univ Technol Sch Comp Guangzhou Peoples R China

A new estimation of distribution algorithm based on normalized mutual information (NMIEDA) is proposed for overcoming the premature convergence of bivariate estimation of distribution algorithms. NMIEDA first uses normalized mutual information to measure the interaction between two variables and then generate a dependency forest model. Second, based on the concept of sporadic model building and a reward and punishment scheme in Selfish Gene, NMIEDA provides a new updating mechanism that accelerates the convergence speed. Finally, a new sampling mechanism is adopted in NMIEDA to improve the efficiency of sampling, which combines stochastic sampling, the opposition-based learning scheme and the mutation operator. The simulation results on benchmark problems and real-world problems demonstrate that NMIEDA often outperforms several other bivariate algorithms.

关键词： estimation of distribution algorithm new sampling mechanism new updating mechanism NMIEDA normalized mutual information

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Maximizing Lifetime of Range-Adjustable Wireless Sensor Networks: A Neighborhood-Based estimation of distribution algorithm

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第11期51卷 5433-5444页

作者： Chen, Zong-Gan Lin, Ying Gong, Yue-Jiao Zhan, Zhi-Hui Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China Sun Yat Sen Univ Dept Psychol Guangzhou 510006 Peoples R China Victoria Univ Melbourne Vic 8001 Australia

Sensor activity scheduling is critical for prolonging the lifetime of wireless sensor networks (WSNs). However, most existing methods assume sensors to have one fixed sensing range. Prevalence of sensors with adjustable sensing ranges posts two new challenges to the topic: 1) expanded search space, due to the rise in the number of possible activation modes and 2) more complex energy allocation, as the sensors differ in the energy consumption rate when using different sensing ranges. These two challenges make it hard to directly solve the lifetime maximization problem of WSNs with range-adjustable sensors (LM-RASs). This article proposes a neighborhood-based estimation of distribution algorithm (NEDA) to address it in a recursive manner. In NEDA, each individual represents a coverage scheme in which the sensors are selectively activated to monitor all the targets. A linear programming (LP) model is built to assign activation time to the schemes in the population so that their sum, the network lifetime, can be maximized conditioned on the current population. Using the activation time derived from LP as individual fitness, the NEDA is driven to seek coverage schemes promising for prolonging the network lifetime. The network lifetime is thus optimized by repeating the steps of the coverage scheme evolution and LP model solving. To encourage the search for diverse coverage schemes, a neighborhood sampling strategy is introduced. Besides, a heuristic repair strategy is designed to fine-tune the existing schemes for further improving the search efficiency. Experimental results on WSNs of different scales show that NEDA outperforms state-of-the-art approaches. It is also expected that NEDA can serve as a potential framework for solving other flexible LP problems that share the same structure with LM-RAS.

关键词： Wireless sensor networks Sensors Sociology Statistics Batteries Energy consumption Resource management estimation of distribution algorithm lifetime maximization neighborhood wireless sensor network (WSN)

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A Surrogate Model Assisted estimation of distribution algorithm with Mutil-acquisition Functions for Expensive Optimization

A Surrogate Model Assisted Estimation of Distribution Algori...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Hao, Hao Wang, Shuai Li, Bingdong Zhou, Aimin East China Normal Univ Shanghai Frontiers Sci Ctr Mol Intelligent Synth Sch Comp Sci & Technol Shanghai Inst AI Educ Shanghai Peoples R China

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

The estimation of distribution algorithm (EDA) is an efficient heuristic method for handling black-box optimization problems since the ability for global population distribution modeling and gradient-free searching. However, the trial and error search mechanism relies on a large number of function evaluations, which is a considerable challenge under expensive black-box problems. Therefore, this article presents a surrogate assisted EDA with multi-acquisition functions. Firstly, a variable-width histogram is used as the global distribution model that focuses on promising areas. Next, the evaluated-free local search method improves the quality of new generation solutions. Finally, model management with multiple acquisitions maintains global and local exploration preferences. Several commonly used benchmark functions with 20 and 50 dimensions are adopted to evaluate the proposed algorithm compared with several state-of-the-art surrogate assisted evaluation algorithms (SAEAs) and Bayesian optimization method. In addition, a rover trajectories optimizing problem is used to verify the ability to solve complex problems. The experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms.

关键词： Surrogate assisted Evolutionary algorithm estimation of distribution algorithm Acquisition functions

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