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Annual Conference of Genetic and Evolutionary Computation Conference

作者： Zhou, Aiming Zhang, Qingfu Jin, Yaochu Sendhoff, Bernhard Tsang, Edward Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England Honda Res Inst Europe D-63073 Offenbach Germany

ISBN: (纸本)9781595936974

Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary agorithms. In this paper, two operators, biased initialization and biased crossover, axe proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population;biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.

关键词： global optimization multiobjective optimization estimation of distribution algorithm biased initialization biased crossover

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A Hybrid Quantum estimation of distribution algorithm (Q-EDA) for Flow-Shop Scheduling

A Hybrid Quantum Estimation of Distribution Algorithm (Q-EDA...

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9th International Conference on Natural Computation (ICNC)

作者： Latif, Muhammad Shahid Zhou, Hong Amir, Muhammad Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781467347143

Intrinsically, the Permutation Flow-Shop Scheduling Problem (PFSSP) is a typical combinatorial optimization problem. It encompasses a strong scientific and engineering background and remains a NP-hard problem over decades. Scheduling and sequencing have played a vital role and had massive applications in modern industries and manufacturing systems. Therefore in order to improve and enhance the performance and efficiency of industrial manufacturing systems in present competitive era, it is worthwhile to develop effective scheduling techniques and approaches. In this paper, a hybrid approach is proposed which is based on standard Quantum Genetic algorithm (QGA) and estimation of distribution algorithm (EDA), aiming at permutation flow-shop scheduling problems (PFSSP). The quantum population is merged with population produced by EDA with a comparative criterion to ensure that the best individual will remain from both populations. The EDA is integrated with standard QGA to produced fitter populations and guide QGA to find promising solution space. Utilizing the advantages of both algorithms, a faster and efficient algorithm is developed, which has produced better results than previous similar approaches for medium scale problems.

关键词： quantum genetic algoritm estimation of distribution algorithm flow shop sccheduling

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Application of estimation of distribution algorithm in HW/SW Partition

Application of Estimation of Distribution Algorithm in HW/SW...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Yu, Juan He, Yuyao Li, Xiaoqiang Northwestern Polytech Univ Sch Marine Sci & Technol Xian Shaanxi Peoples R China Xian Univ Technol Sch Automat & Informaton Engn Xian Shaanxi Peoples R China

ISBN: (纸本)9781479952748

Hardware/software (HW/SW) partitioning problem is NP hard problem. An improved algorithm based on estimation of distribution algorithms is proposed to solve HW/SW partitioning problem. estimation of distribution algorithm is good in globe search but poor in local search and may suffer from" premature convergence" beacause of diversity loss. The improved algorithm strengthens the local searching ability by cloning and searching the elite solutions and improves the diversity loss by correcting the probability model. Numerical simulation is carried out and compared with existing algorithm, the results show the effectiveness of the improved estimation of distribution algorithm in solving HW/SW partitioning problem.

关键词： hardware/software partitioning estimation of distribution algorithm elite clone probability model correction

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Hybrid estimation of distribution algorithm for Blocking Flow-Shop Scheduling Problem with Sequence-Dependent Setup Times 14th

Hybrid Estimation of Distribution Algorithm for Blocking Flo...

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14th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Zi-Qi Qian, Bin Liu, Bo Hu, Rong Zhang, Chang-Sheng Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

ISBN: (纸本)9783319959290;9783319959306

This paper presents an innovative hybrid estimation of distribution algorithm, named HEDA, for blocking flow-shop scheduling problem (BFSP) with sequence-dependent setup times (SDSTs) to minimize the makespan criterion, which has been proved to be typically NP-hard combinatorial optimization problem with strong engineering background. Firstly, several efficient heuristics are proposed according to the property of BFSP with SDSTs. Secondly, the genetic information of both the order of jobs and the promising blocks of jobs are concerned to generate the guided probabilistic model. Thirdly, after the HEDA-based global exploration, a reference sequence-based local search with path relinking technique is developed and incorporated into local exploitation to escape from local optima and improve the convergence property. Due to the reasonable balance between EDA-based global exploration and sequence dependent local exploitation as well as comprehensive utilization of the speedup evaluation method, the BFSP with SDSTs can be solved effectively and efficiently. Finally, computational results and comparisons with the existing state-of-the-art algorithms are carried out, which demonstrate the superiority of the proposed HEDA in terms of searching quality, robustness, and efficiency.

关键词： estimation of distribution algorithm Blocking flow-shop scheduling problem Sequence-dependent setup times Path relinking

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A Compact estimation of distribution algorithm for Solving Hybrid Flow-shop Scheduling Problem

A Compact Estimation of Distribution Algorithm for Solving H...

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10th World Congress on Intelligent Control and Automation (WCICA)

作者： Wang, Shengyao Wang, Ling Xu, Ye Tsinghua Univ Dept Automat TNList Beijing Peoples R China

ISBN: (纸本)9781467313988

According to the characteristics of the hybrid flow-shop scheduling problem (HFSP), the permutation based encoding and decoding schemes are designed and a probability model for describing the distribution of the solution space is built to propose a compact estimation of distribution algorithm (cEDA) in this paper. The algorithm uses only two individuals by sampling based on the probability model and updates the parameters of the probability model with the selected individual. The cEDA is efficient and easy to implement due to its low complexity and comparatively few parameters. Simulation results based on some widely-used instances and comparisons with some existing algorithms demonstrate the effectiveness and efficiency of the proposed compact estimation of distribution algorithm. The influence of the key parameter on the performance is investigated as well.

关键词： Hybrid flow-shop scheduling estimation of distribution algorithm probability model compact algorithm

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Hyper-heuristic estimation of distribution algorithm for Green Hybrid Flow-Shop Scheduling and Transportation Integrated Optimization Problem 19th

Hyper-heuristic Estimation of Distribution Algorithm for Gre...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Bai, Ling Qian, Bin Hu, Rong Li, Zuocheng Jin, Huai-Ping Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

With the deepening of economic globalization, the integrated production and transportation mode has become an inevitable tendency of modern supply chain. At the same time, facing increasingly serious ecological and environmental problems, green production and green transportation are important ways to reduce carbon emissions. In this study, a green hybrid flow-shop scheduling and transportation integrated optimization problem (GHFSSTIOP) with the objective of minimizing total cost is investigated. To cope with this problem, we present a hyper-heuristic estimation of distribution algorithm (HHEDA). According to the features of GHFSSTIOP, firstly, a novel loading strategy is proposed. Secondly, estimation of distribution algorithm (EDA) is used for high-level strategy, to learn and accumulate the sequence information of high-quality solutions and their location information in the high-level population, and then generate new high-level solutions by sampling probabilistic model in EDA to enhance the global search ability of HHEDA;subsequently, four effective low-level heuristic operations are designed in the low-level of HHEDA to enhance the local search capability of HHEDA, and a new update strategy is designed to ensure variegation of high-level population. Finally, effectiveness of HHEDA is evidenced by numerical simulation and algorithm comparison.

关键词： Green hybrid flow-shop Production and transportation integration scheduling Hyper-heuristic estimation of distribution algorithm

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Training Artificial Neural Networks Via estimation of distribution algorithm

Training Artificial Neural Networks Via Estimation of Distri...

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2nd International Conference on Modelling and Simulation

作者： Liao, Weifang Jiang, Yong Song, Changhui Nanjing Univ Sci & Technol Nanjing 210094 Peoples R China

Apply estimation of distribution algorithm (EDA) to optimize the weights of neural networks, solving the problem of traditional BP algorithm convergence rate is too slow and easy to fall into the local minimum solutio... 详细信息

ISBN: (纸本)9781846260681

关键词： estimation of distribution algorithm Neural network Optimize

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A new algorithm based on the Gbest of Particle Swarm Optimization algorithm to improve estimation of distribution algorithm

A new Algorithm based on the Gbest of Particle Swarm Optimiz...

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International Conference on Smart Computing and Electronic Enterprise (ICSCEE)

作者： Zhao, Qiuyue Gao, Ying Guangzhou Univ Comp Sci & Educ Software Inst Guangzhou Peoples R China

ISBN: (纸本)9781538648384

In recent years, with the rise of artificial intelligence and deep learning, as an evolutionary algorithm based on probability model, estimation of distribution algorithm has been widely research and development. The estimation of distribution algorithm without the traditional genetic operation such as crossover and mutation, is a new kind of evolution model. As an algorithm based on probabilistic mode, the estimation of distribution algorithm establishes a probabilistic model describing the solution space of optimization problems. With the emergence for big data, the convergence of the algorithm and the requirements for solving precision are also increasing. This paper attempts to improve the distribution estimation algorithm. The optimal population of each iteration is found through the location update of each iteration of the Particle Swarm Optimization (PSO) algorithm. The simulation test was carried out with ten benchmark test function. The proposed algorithm was compared with the GA_EDA9improved genetic algorithm) and the basic distribution estimation (EDA) algorithm. Experimental results show that the new algorithm is superior to GA_EDA and basic EDA in terms of convergence and accuracy.

关键词： estimation of distribution algorithm particle Swarm Optimization algorithm simulation

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Sampling in Latent Space for a Mulitiobjective estimation of distribution algorithm

Sampling in Latent Space for a Mulitiobjective Estimation of...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Dong, Bing Zhou, Aimin Zhang, Guixu East China Normal Univ Dept Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc 500 Dongchuan Rd Shanghai Peoples R China

ISBN: (纸本)9781509006229

A regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) has been proposed for continuous multiobjective optimization problems. Generating promising solutions to approximate the population is significant to RM-MEDA. In the reproduction of RM-MEDA, it adopts a Latin square design strategy to sample points in the latent space that is extended to cover the whole Pareto set. However, the setting of the extension scale is problem-dependent to some extent. To circumvent this issue, we propose a differential evolution based sampling (DES) scheme for RM-MEDA. DES mutates the projections of the parent solutions in the latent space to generate promising candidate offspring solutions. The empirical experiment results have shown the significant advantages of the DES scheme comparing to the Latin square design.

关键词： estimation of distribution algorithm multiobjective optimization latent space differential evolution

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An enhanced two phase estimation of distribution algorithm for solving scheduling problem

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2023年第3期18卷 217-224页

作者： Hao, Xinchang Tian, Jing Ding, Hui Zhao, Keheng Gen, Mitsuo Changzhou Inst Technol Sch Art & Design Changzhou Jiangsu Peoples R China State Key Lab Air Traff Management Syst & Technol 1 Yongzhi Rd Nanjing Jiangsu Peoples R China Fuzzy Log Syst Inst Tokyo Japan Tokyo Univ Sci Tokyo Japan

Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid estimation of distribution algorithm (EDA) with Teaching and Learning-Based Optimization algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency.

关键词： Metaheuristic optimization estimation of distribution algorithm teaching and learning based optimization algorithm scheduling optimization

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