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An improved estimation of distribution algorithm for rescue task emergency scheduling considering stochastic deterioration of the injured

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COMPLEX & INTELLIGENT SYSTEMS 2024年第1期10卷 413-434页

作者： Xu, Ying Li, Xiaobo Li, Qian Zhang, Weipeng Ningbo Univ Fac Elect Engn & Comp Sci Ningbo Zhejiang Peoples R China Ningbo Univ Finance & Econ Coll Digital Technol & Engineer Ningbo 315175 Zhejiang Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Zhejiang Peoples R China

Efficient allocating and scheduling emergency rescue tasks are a primary issue for emergency management. This paper considers emergency scheduling of rescue tasks under stochastic deterioration of the injured. First, a mathematical model is established to minimize the average mathematical expectation of all tasks' completion time and casualty loss. Second, an improved multi-objective estimation of distribution algorithm (IMEDA) is proposed to solve this problem. In the IMDEA, an effective initialization strategy is designed for obtaining a superior population. Then, three statistical models are constructed, which include two tasks existing in the same rescue team, the probability of first task being processed by a rescue team, and the adjacency between two tasks. Afterward, an improved sampling method based on referenced sequence is employed to efficiently generate offspring population. Three multi-objective local search methods are presented to improve the exploitation in promising areas around elite individuals. Furthermore, the parameter calibration and effectiveness of components of IMEDA are tested through experiments. Finally, the comprehensive comparison with state-of-the-art multi-objective algorithms demonstrates that IMEDA is a high-performing approach for the considered problem.

关键词： Allocating Scheduling Stochastic deterioration estimation of distribution algorithm Local search

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An estimation of distribution algorithm for lot-streaming flow shop problems with setup times

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2012年第2期40卷 166-180页

作者： Pan, Quan-Ke Ruiz, Ruben Univ Politecn Valencia Ciudad Politecn Innovac Inst Tecnol Informat Grp Sistemas Optimizac Aplicada Valencia 46022 Spain Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China

Lot-streaming flow shops have important applications in different industries including textile, plastic, chemical, semiconductor and many others. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and no-idling production cases. The objective is to minimize the maximum completion time or makespan. To solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation. In the proposed EDA, an efficient initialization scheme based on the NEH heuristic is presented to construct an initial population with a certain level of quality and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search towards good solutions by taking into account both job permutation and similar blocks of jobs. A simple but effective local search is added to enhance the intensification capability. A diversity controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up method is presented to reduce the computational effort needed for the local search technique and the NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms from the literature. The results show that the proposed EDA is very effective in comparison after comprehensive computational and statistical analyses. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Flow shop scheduling Lot-streaming estimation of distribution algorithm Makespan Sequence-dependent setup times

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An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2013年第1期145卷 387-396页

作者： Wang, Sheng-yao Wang, Ling Liu, Min Xu, Ye Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 10084 Peoples R China

In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, the earliest completion factory rule is employed for the permutation based encoding to generate feasible schedules and calculate the schedule objective value. Then, a probability model is built for describing the probability distribution of the solution space, and a mechanism is provided to update the probability model with superior individuals. By sampling the probability model, new individuals can be generated among the promising search region. Moreover, to enhance the local exploitation, some local search operators are designed based on the problem characteristics and utilized for the promising individuals. In addition, the influence of parameter setting of the EDA is investigated based on the Taguchi method of design of experiments, and a suitable parameter setting is suggested. Finally, numerical simulations based on 420 small-sized instances and 720 large-sized instances are carried out. The comparative results with some existing algorithms demonstrate the effectiveness of the proposed EDA in solving the DPFSP. In addition, the new best-known solutions for 17 out of 420 small instances and 589 out of 720 large instances are found. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Distributed permutation flow-shop scheduling estimation of distribution algorithm Probability model Design of experiment

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estimation of distribution algorithm Based on Multivariate Gaussian Copulas

Estimation of Distribution Algorithm Based on Multivariate G...

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The 2010 IEEE International Conference on Progress in Informatics and Computing

作者： Ying Gao,Xiao Hu,Huiliang Liu Department of Computer Science and Technology,Guangzhou University Guangzhou,510006,P.R.of China

Copula is a powerful tool for multivariate probability *** of distribution algorithms are a class of optimization algorithms based on probability distribution model. This paper introduces a new estimation of distribution algorithm with multivariate Gaussian *** the algorithm,Gaussian copula parameters are firstly estimated by estimating Kendall's tau and using the relationship of Kendall's tau and correlation matrix,thus,joint distribution is estimated. Then,the Monte Carte simulation is used to generate new *** relative experimental results show that the new algorithm is effective.

关键词： estimation of distribution algorithm Gaussian copulas Kendall's tau Monte Carte simulation

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GAIT GENERATION AND OPTIMIZATION USING THE estimation of distribution algorithm FOR TEENSIZE HUMANOID SOCCER ROBOT RESr-1

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INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS 2008年第3期5卷 437-456页

作者： Hu, Lingyun Zhou, Changjiu Singapore Polytechn ARICC Singapore 139651 Singapore

This paper gives an overview of locomotion planning and control of a TeenSize humanoid soccer robot, Robo-Erectus Senior (RESr-1), which has been developed as an experimental platform for human-robot interaction and cooperative research in general and robotics soccer games in particular. The locomotion planning and control, along with an introduction of hierarchical control architecture, vision-based behavior and its application in the Humanoid TeenSize soccer challenge, are elaborated. The estimation of distribution algorithm (EDA) is used in locomotion generation and optimization to achieves dynamically stable walk and a powerful kick. By setting different objective functions, smooth walking and powerful kicking can be generated quickly. RESr-1 made its debut at RoboCup 2007, and got fourth place in the Humanoid TeenSize penalty kick competition. In addition, some experimental results on RESr-1's walking, tracking and kicking are presented.

关键词： estimation of distribution algorithm full-scale humanoid robot walking and kicking gait generation and optimization humanoid soccer robotics system

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An estimation of distribution algorithm for energy-aware Steelmaking Continuous Casting Scheduling

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3rd International Conference on Machinery Electronics and Control Engineering (ICMECE 2013)

作者： Wang, Guirong Li, Qiqiang Yuan, Maorong Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Shandong Jianzhu Univ Sch Thermal Energy Engn Jinan Peoples R China

ISBN: (纸本)9783037859032

This paper addresses the Steelmaking Continuous Casting production scheduling problem(SCC) with power consumption as the main objective. An encoding and decoding scheme is proposed and an effective estimation of distribution algorithm (EDA) is presented to solve it. Simulation experiments indicate that EDA can solve the SCC problem efficiently and has fast speed of convergency.

关键词： Steelmaking Continuous Casting Scheduling estimation of distribution algorithm power consumption

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Opposition-Based Learning estimation of distribution algorithm with Gaussian Copulas and Its Application to Placement of RFID Readers

Opposition-Based Learning Estimation of Distribution Algorit...

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3rd International Conference on Artificial Intelligence and Computational Intelligence (AICI 2011)

作者： Gao, Ying Hu, Xiao Liu, Huiliang Li, Fufang Peng, Lingxi Guangzhou Univ Dept Comp Sci & Technol Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783642238802

estimation of distribution algorithms are a class of optimization algorithms based on probability distribution model. In this paper, we propose an improved estimation of distribution algorithm using opposition-based learning and Gaussian copulas. The improved algorithm employs multivariate Gaussian copulas to construct probability distribution model and uses opposition-based learning for population initialization and new population generation. By estimating Kendall's tau and using the relationship of Kendall's tau and correlation matrix, Gaussian copula parameters are firstly estimated, thus, joint distribution is estimated. Afterwards, the Monte Carte simulation is used to generate new individuals. Then, the opposite numbers have also been utilized to improve the convergence performances. The improved algorithm is applied to some benchmark functions and optimal placement of readers in RFID networks. The relative experimental results show that the improved algorithm has better performance than original version of estimation of distribution algorithm and is effective in the optimal placement of readers in REID networks.

关键词： estimation of distribution algorithm Opposition-based learning Gaussian copulas RFID networks

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RK-EDA: A Novel Random Key Based estimation of distribution algorithm

RK-EDA: A Novel Random Key Based Estimation of Distribution ...

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14th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Ayodele, Mayowa McCall, John Regnier-Coudert, Olivier Robert Gordon Univ Aberdeen Scotland

ISBN: (纸本)9783319458236;9783319458229

The challenges of solving problems naturally represented as permutations by estimation of distribution algorithms (EDAs) have been a recent focus of interest in the evolutionary computation community. One of the most common alternative representations for permutation based problems is the Random Key (RK), which enables the use of continuous approaches for this problem domain. However, the use of RK in EDAs have not produced competitive results to date and more recent research on permutation based EDAs have focused on creating superior algorithms with specially adapted representations. In this paper, we present RK-EDA;a novel RK based EDA that uses a cooling scheme to balance the exploration and exploitation of a search space by controlling the variance in its probabilistic model. Unlike the general performance of RK based EDAs, RK-EDA is actually competitive with the best EDAs on common permutation test problems: Flow Shop Scheduling, Linear Ordering, Quadratic Assignment, and Travelling Salesman Problems.

关键词： estimation of distribution algorithm Random key Permutation problems Cooling scheme Univariate model

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An Improved estimation of distribution algorithm for Cloud Computing Resource Scheduling 10

An Improved Estimation of Distribution Algorithm for Cloud C...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Sun, Haisheng Liu, Chuang Xu, Rui Chen, Huaping Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China Univ Sci & Technol China Sch Management Hefei Anhui Peoples R China Hohai Univ Sch Business Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538643624

This paper focuses on cloud computing resource scheduling on the Soft as a Service layer and aims at minimizing the user costs by regarding the deadline as a constraint for scheduling independent tasks. Existing works with evolutionary computation approaches fail to describe the interactions among independent tasks. To overcome this problem, an improved Markov-chain-based estimation of distribution algorithm is proposed, and the concept of virtual machine selection diversity is created to construct the probabilistic model rationally. Moreover, one heuristic rule related to the investigated problem is created to keep the population maintaining a high diversity in the evolution process. The experiment results show that the proposed algorithm not only obtains the best solution quality but also has competitive convergence among all compared algorithms.

关键词： estimation of distribution algorithm markov chain cloud computing resource scheduling

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An estimation of distribution algorithm based on the Natural Gradient and the Boltzmann distribution 15

An Estimation of Distribution Algorithm based on the Natural...

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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Segovia-Dominguez, Ignacio Hernandez-Aguirre, Arturo Ctr Res Math Guanajuato Mexico

ISBN: (纸本)9781450334723

This paper introduces an estimation of distribution algorithm (EDA), in which the parameters of the search distribution are updated by the natural gradient technique. The parameter updating is guided via the Kullback-Leibler divergence between the multivariate Normal and the Boltzmann densities. This approach makes sense because it is well-known that the Boltzmann function yields a reliable model to simulate particles near to optimum locations. Three main contributions are presented here in order to build an effective EDA. The first one is a natural gradient formula which allows for an update of the parameters of a density function. These equations are related to an exponential parametrization of the search distribution. The second contribution involves the approximation of the developed gradient formula and its connection to the importance sampling method. The third contribution is a parameter update rule which is designed to control the exploration and exploitation phases of the algorithm. The proposed EDA is tested on a benchmark of 16 problems and compared versus the XNES and iAMaLGaM algorithms. The statistical results show that the performance of the proposed method is competitive and it is the winner in several problems.

关键词： estimation of distribution algorithm Boltzmann distribution Natural Gradient Optimization

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