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An improved EDA for solving Steiner tree problem

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2015年第13期27卷 3483-3496页

作者： Liu, Lei Wang, Hua Kong, Guohong Shandong Univ Sch Comp Sci & Technol Jinan 250100 Shandong Peoples R China

Steiner tree problem and its derivations are widely employed to optimize the design of transportation, communication networks, biological engineering and the QoS multicast routing problem. It is one of well-defined open issues which have attracted many research efforts. Different from the existing works, this paper develops a new method of solving Steiner tree problem by using the improved estimation of distribution algorithms (EDA). Further, the performance of developed method is validated by applying on multicast routing optimization. The developed method randomly initializes n trees which contain the source node and the destination nodes. And some individuals select the crossover operation randomly to add the population diversity and avoid the algorithm premature convergence. The algorithm constructs a probabilistic model according to the selected elites, which is capable of estimating the probability distribution of the solution. The probabilistic model is updated according to the new population. New trees are generated based on the probabilistic model. This process iterated until designated termination criteria are met. The improved EDA algorithm gradually evolves trees to obtain a better solution. Simulation validations suggest that the developed method leads to better performance. In particular, the complexity in terms of the converging speed improves significantly compared to other algorithms. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： Steiner tree estimation of distribution algorithm approximation algorithm intelligence optimization probabilistic model

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An Improved Population-Based Incremental Learning Method for Objects Buried in Planar Layered Media

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IEEE TRANSACTIONS ON MAGNETICS 2012年第2期48卷 1027-1030页

作者： Chen, Xiaoming Lei, Gang Yang, Guangyuan Shao, K. R. Guo, Youguang Zhu, Jianguo Lavers, J. D. Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

An evolutionary algorithm, the estimation of distribution algorithm (EDA), is used to reconstruct the objects that buried in planar layered media. It is essential that fast forward solvers be used to solve the forward scattering problem for the nonlinear inverse scattering methods, since it can avoid errors by approximation. The EDA is a predominant all-round optimizing method in the macroscopic simulation of evolution process species of nature. Recent studies have shown that the EDA provides better solution for nonlinear problems than the microscopic evolutionary algorithm, such as genetic algorithm (GA) in some cases. The EDA is simpler, both computationally and theoretically, than the GA. We discuss how this can be used to calculate the permittivity and conductivity of the targets. We show preliminary results indicating the potential of reconstruction for buried objects. Compared with other methods, the experiment result shows that the EDA algorithm reduces the number of iteration.

关键词： Buried objects estimation of distribution algorithm inverse scattering layered media

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Information Fusion in Offspring Generation: A Case Study in Gene Expression Programming

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IEEE ACCESS 2020年 8卷 74782-74792页

作者： Liu, Tonglin Zhang, Hengzhe Zhang, Hu Zhou, Aimin Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai 200062 Peoples R China

Gene expression programming (GEP), which is a variant of genetic programming (GP) with a fixed-length linear model, has been applied in many domains. Typically, GEP uses genetic operators to generate offspring. In recent years, the estimation of distribution algorithm (EDA) has also been proven to be efficient for offspring generation. Genetic operators such as crossover and mutation generate offspring from an implicit model by using the individual information. By contrast, EDA operators generate offspring from an explicit model by using the population distribution information. Since both the individual and population distribution information are useful in offspring generation, it is natural to hybrid EDA and genetic operators to improve the search efficiency. To this end, we propose a hybrid offspring generation strategy for GEP by using a univariate categorical distribution based EDA operator and its original genetic operators. To evaluate the performance of the new hybrid algorithm, we apply the algorithm to ten regression tasks using various parameters and strategies. The experimental results demonstrate that the new algorithm is a promising approach for solving regression problems efficiently. The GEP with hybrid operators outperforms the original GEP that uses genetic operators on eight out of ten benchmark datasets.

关键词： Gene expression Sociology Statistics Probabilistic logic Genetic algorithms Programming Genetic programming gene expression programming estimation of distribution algorithm offspring generation

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Data-driven topology design using a deep generative model

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2021年第3期64卷 1401-1420页

作者： Yamasaki, Shintaro Yaji, Kentaro Fujita, Kikuo Osaka Univ Dept Mech Engn 2-1 Yamadaoka Suita Osaka 5650871 Japan

In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.

关键词： Data-driven design Topology optimization Deep generative model Sensitivity-free methodology Multi-objective methodology estimation of distribution algorithm

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Two-stage EDA-based approach for all optical WDM mesh network survivability under SRLG constraints

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APPLIED SOFT COMPUTING 2011年第1期11卷 916-926页

作者： Sun, Jianyong Univ Nottingham Sch Biosci CPIB Nottingham NG7 2RD England

In this paper, a two-stage evolutionary algorithm is proposed to solve an NP-complete telecommunication problem-all optical wavelength-division multiplexing (WDM) mesh network survivability under shared-risk-link-group (SRLG) constraints. First of all, a novel greedy heuristic with two control parameters is developed to construct feasible solutions of the telecommunication problem. An estimation of distribution algorithm (EDA) with guided mutation is applied to search for optimum settings of the two control parameters in respective two stages. Given the found best control parameters, an optimal solution of the considered problem can be constructed by the greedy heuristic. Experimental results show that the proposed approach compares favorably against the best-known evolutionary-based algorithm in 26 out of 30 test instances in terms of solution quality within given time limit. (c) 2010 Elsevier B. V. All rights reserved.

关键词： Shared-risk-link-group Routing and wavelength assignment problem Greedy heuristic estimation of distribution algorithm

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Shared manufacturing-based distributed flexible job shop scheduling with supply-demand matching

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 189卷

作者： Wei, Guangyan Ye, Chunming Xu, Jianning Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

Shared Manufacturing (sharedMfg) is a peer-to-peer (P2P) paradigm for sharing manufacturing resources, derived from the sharing economy. In sharedMfg, manufacturing service resources are integrated into a large set with defined service types. Based on its resource organization structure, we build a model for the shared manufacturing-based distributed flexible job shop scheduling problem (SM-DFJSP) with supply-demand matching. The goal is to minimize both the total cost and the makespan. The SM-DFJSP model enables the scheduling of jobs requiring different manufacturing services across distributed, heterogeneous, and flexible service resource units (SRUs) with diverse manufacturing functions. To solve the SM-DFJSP, we propose a hybrid estimation of distribution algorithm and Tabu search (EDA-TS), including EDA and TS components. Additionally, a multi-populations strategy and non-dominated solutions memory mechanism are designed to improve the exploration ability of the algorithm. Within the EDA component, three probability distribution models and some dispatching rules are designed to generate a new population. In the TS component, three neighborhood search structures are built that adopt hybrid short and long memory tabu strategy. Finally, comparison and ablation experiments on 25 instances demonstrate the superior performance of the EDA-TS algorithm in solving the SM-DFJSP, highlighting the effectiveness of the multi-population strategy and non-dominated solutions memory mechanism.

关键词： Shared manufacturing Distributed flexible job shop scheduling estimation of distribution algorithm Tabu search

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Optimal chiller loading in HVAC System Using a Novel algorithm Based on the distributed framework

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JOURNAL OF BUILDING ENGINEERING 2020年 28卷

作者： Yu, Junqi Liu, Qite Zhao, Anjun Qian, Xuegen Zhang, Rui Xian Univ Architecture & Technol Xian Shaanxi Peoples R China

Aimed at the optimal chiller loading (OCL) problem in heating, ventilation and air conditioning (HVAC) system, a distributed framework for HVAC control is introduced and discussed. And furthermore, the distributed chaotic estimation of distribution algorithm (DCEDA) based on this framework is proposed. Firstly, compared with the centralized framework, the distributed framework has the features of flexibility and expansibility. Therefore, the distributed framework can fit the development of HVAC control system better. Secondly, in the proposed algorithm, an initialization methodology based on logistic map is developed and the chaotic mutation mechanism is applied to increase the search capability of the algorithm. To testify the performance of DCEDA, two well-known cases based on the OCL problem are tested and the results are compared with other algorithms. The results show that DCEDA is an efficient distributed optimization algorithm with good robustness, stability and convergence, and it can achieve significant energy saving effect.

关键词： Optimal chiller loading Distributed framework estimation of distribution algorithm Energy saving

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A review on probabilistic graphical models in evolutionary computation

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JOURNAL OF HEURISTICS 2012年第5期18卷 795-819页

作者： Larranaga, Pedro Karshenas, Hossein Bielza, Concha Santana, Roberto Univ Politecn Madrid Fac Informat Computat Intelligence Grp E-28660 Madrid Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastin 20080 Donostia Spain

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

关键词： Probabilistic graphical model Bayesian network Evolutionary computation estimation of distribution algorithm

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A hybrid evolutionary algorithm with guided mutation for minimum weight dominating set

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APPLIED INTELLIGENCE 2015年第3期43卷 512-529页

作者： Chaurasia, Sachchida Nand Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

This paper presents a hybrid evolutionary algorithm with guided mutation (EA/G) to solve the minimum weight dominating set problem (MWDS) which is -hard in nature not only for general graphs, but also for unit disk graphs (UDG). MWDS finds practical applications in diverse domains such as clustering in wireless networks, intrusion detection in adhoc networks, multi-document summarization in information retrieval, query selection in web databases etc. EA/G is a recently proposed evolutionary algorithm that tries to overcome the shortcomings of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) both, and that can be considered as a cross between the two. The solution obtained through EA/G algorithm is further improved through an improvement operator. We have compared the performance of our hybrid evolutionary approach with the state-of-the-art approaches on general graphs as well as on UDG. Computational results show the superiority of our approach in terms of solution quality as well as execution time.

关键词： Constrained optimization Dominating set estimation of distribution algorithm Evolutionary algorithm Guided mutation Heuristic

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A bi-population EDA for solving the no-idle permutation flow-shop scheduling problem with the total tardiness criterion

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KNOWLEDGE-BASED SYSTEMS 2015年第Jan.期74卷 167-175页

作者： Shen, Jing-nan Wang, Ling Wang, Sheng-yao Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, an effective bi-population estimation of distribution algorithm (BEDA) is presented to solve the no-idle permutation flow-shop scheduling problem (NIPFSP) with the total tardiness criterion. To enhance the search efficiency and maintain the diversity of the whole population, two sub-populations are used in the BEDA. The two sub-populations are generated by sampling the probability models that are updated differently for the global exploration and the local exploitation, respectively. Meanwhile, the two sub-populations collaborate with each other to share search information for adjusting the models. To well adjust the models for generating promising solutions, the global probability model is updated during the evolution with the superior population and the local probability model is updated with the best solution that has been explored. To further enhance exploitation in the promising region, the insertion operator is used iteratively as the local search procedure. To investigate the influence of parameter setting, numerical study based on the Taguchi method of design-of-experiment is carried out. The effectiveness of the hi-population strategy and local search procedure is shown by numerical comparisons, and the comparisons with the recently published algorithms by using the benchmarking instances also demonstrate the effectiveness of the proposed BEDA. (C) 2014 Elsevier B.V. All rights reserved.

关键词： No-idle permutation flow-shop scheduling problem Total tardiness estimation of distribution algorithm Bi-population Probability model

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