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Adaptive Memetic Computing for Evolutionary Multiobjective Optimization

IEEE TRANSACTIONS ON CYBERNETICS 2015年第4期45卷 610-621页

作者： Shim, Vui Ann Tan, Kay Chen Tang, Huajin Inst Infocomm Res Singapore 138632 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

Inspired by biological evolution, a plethora of algorithms with evolutionary features have been proposed. These algorithms have strengths in certain aspects, thus yielding better optimization performance in a particular problem. However, in a wide range of problems, none of them are superior to one another. Synergetic combination of these algorithms is one of the potential ways to ameliorate their search ability. Based on this idea, this paper proposes an adaptive memetic computing as the synergy of a genetic algorithm, differential evolution, and estimation of distribution algorithm. The ratio of the number of fitter solutions produced by the algorithms in a generation defines their adaptability features in the next generation. Subsequently, a subset of solutions undergoes local search using the evolutionary gradient search algorithm. This memetic technique is then implemented in two prominent frameworks of multiobjective optimization: the domination- and decomposition-based frameworks. The performance of the adaptive memetic algorithms is validated in a wide range of test problems with different characteristics and difficulties.

关键词： Differential evolution estimation of distribution algorithm evolutionary gradient search genetic algorithm memetic computing multiobjective optimization

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A distribution evolutionary algorithm for the graph coloring problem

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 80卷

作者： Xu, Yongjian Cheng, Huabin Xu, Ning Chen, Yu Xie, Chengwang Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Wuchang Shouyi Univ Dept Basic Sci Wuhan 430064 Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China South China Normal Univ Sch Data Sci & Engn Guangzhou 516600 Guangdong Peoples R China

Graph coloring is a challenging combinatorial optimization problem with a wide range of applications. In this paper, a distribution evolutionary algorithm based on a population of probability model (DEA-PPM) is developed to address it efficiently. Unlike existing estimation of distribution algorithms where a probability model is updated by generated solutions, DEA-PPM employs a distribution population based on a novel probability model, and an orthogonal exploration strategy is introduced to search the distribution space with the assistance of an refinement strategy. By sampling the distribution population, efficient search in the solution space is realized based on a tabu search process. Meanwhile, DEA-PPM introduces an iterative vertex removal strategy to improve the efficiency of k-coloring, and an inherited initialization strategy is implemented to address the chromatic number problem well. The cooperative evolution of the distribution population and the solution population leads to a good balance between exploration and exploitation. Numerical results demonstrate that the DEA-PPM of small population size is competitive to the state-of-the-art metaheuristics.

关键词： distribution evolutionary algorithm Orthogonal exploration Inherited initialization Graph coloring estimation of distribution algorithm

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Double-Objective Optimal Control for Non-Gaussian Systems: An Example Study on Analytical vs Numerical Solutions

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IFAC-PapersOnLine 2015年第8期48卷 924-929页

作者： Mifeng Ren Jianhua Zhang Hong Wang Min Huang College of Information Engineering Taiyuan University of Technology Taiyuan 030024 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China Control System Centre School of Electrical and Electronics Engineering The University of Manchester M60 1QD UK China State Key Laboratory of Synthetical Automation for Process Industry Northeastern University Shenyang 110819 China

Minimum entropy control has been proven to be an effective method in control of non-Gaussian stochastic systems. In this case, the entropy is proposed as a generalization of the variance measure to characterize the randomness of the process. Minimum entropy corresponds to small uncertainty (or derivation), but it cannot guarantee the tracking error approaching to zero. Therefore, mean square error also should be added in the criterion. In this paper, by using a simple example, the method of generating a representative approximation of the Pareto optimal control set is investigated in both analytical and numerical ways. And simulation results show the feasibility of the proposed double-objective optimal control method.

关键词： Double-objective optimal control non-Gaussian stochastic systems Renyi's entropy e - constrained method estimation of distribution algorithm

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Variance analysis and adaptive control in intelligent system based on Gaussian model

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2013年第1期18卷 26-33页

作者： Liu, Jun Wang, Yishou Teng, Hongfei Dalian Polytech Univ Sch Mech Engn & Automat Dalian 116034 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

estimation of distribution algorithms (EDAs) are intelligent systems that take advantage of statistical learning techniques. The distribution of promising regions in the search space is estimated and probabilistically guide the new particles' searching towards them in EDAs. But EDAs cannot solve complex optimisation problems reliably and efficiently because of premature convergence and difficulty of complex probabilistic model learning. This paper presents a PCA-EDA algorithm which introduces principal component analysis (PCA) into simple Gaussian model-based EDA. PCA-EDA is aimed to keep the balance of accuracy and efficiency of probabilistic model learning, as well as to avoid premature convergence by PCA's ability of analysis of Gaussian model's variance. From the results of numerical experiments, it is showed that the proposed method is feasible and effective for complex optimisation problems.

关键词： principle component analysis PCA estimation of distribution algorithm EDA intelligent system adaptive variance control

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The maximal covering location problem with accessibility indicators and mobile units

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SOCIO-ECONOMIC PLANNING SCIENCES 2023年第PartB期87卷

作者： Vicencio-Medina, Salvador J. Rios-Solis, Yasmin A. Ibarra-Rojas, Omar Jorge Cid-Garcia, Nestor M. Rios-Solis, Leonardo Tecnol Monterrey Sch Engn & Sci Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico Univ Autonoma Nuevo Leon Fac Ciencias Fisico Matemat San Nicolas De Los Garza 66455 Nuevo Leon Mexico CONACYT Ctr Invest Ciencias Informac Geoespacial Lab Nacl Geointeligencia Aguascalientes Aguascalientes Mexico Univ Edinburgh Inst Bioengn Sch Engn Edinburgh EH9 3BF Scotland Univ Edinburgh Ctr Synthet & Syst Biol SynthSys Edinburgh EH9 3BF Scotland Newcastle Univ Sch Nat & Environm Sci Mol Biol & Biotechnol Newcastle Upon Tyne NE1 7RU England

We study the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units that maximizes the facilities coverage, the accessibility of the zones to the open facilities, and the spatial disaggregation. The main characteristic of our problem is that mobile units can be deployed from open facilities to extend the coverage, accessibility, and opportunities for the inhabitants of the different demand zones. We formulate the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units as a mixed-integer linear programming model. To solve larger instances, we propose a matheuristic (combination of exact and heuristic methods) composed of an estimation of distribution algorithm and a parameterized Maximal Covering Location Problem with Accessibility Indicators and Mobile Units integer model. To test our methodology, we consider the Maximal Covering Location Problem with Accessibility Indicators and Mobile Units model to cover the low-income zones with Severe Acute Respiratory Syndrome Coronavirus 2 patients. Using official databases, we made a set of instances where we considered the poverty index, number of population, locations of hospitals, and Severe Acute Respiratory Syndrome Coronavirus 2 patients. The experimental results show the efficiency of our methodologies. Compared to the case without mobile units, we drastically improve the coverage and accessibility for the inhabitants of the demand zones.

关键词： Facility location problem Matheuristic COVID-19 Mixed-integer linear program Accessibility estimation of distribution algorithm

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Model-Based Position and Reflectivity estimation of Fiber Bragg Grating Sensor Arrays

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SENSORS 2018年第7期18卷 2268-2268页

作者： Werzinger, Stefan Zibar, Darko Koepel, Max Schmauss, Bernhard Friedrich Alexander Univ Erlangen Nurnberg Inst Microwaves & Photon Cauerstr 9 D-91058 Erlangen Germany Tech Univ Denmark DTU Foton DK-2800 Lyngby Denmark Friedrich Alexander Univ Erlangen Nurnberg Grad Sch Adv Opt Studies SAOT D-91052 Erlangen Germany Max Planck Inst Sci Light Staudtstr 2 D-91058 Erlangen Germany

We propose an efficient model-based signal processing approach for optical fiber sensing with fiber Bragg grating (FBG) arrays. A position estimation based on an estimation of distribution algorithm (EDA) and a reflectivity estimation method using a parametric transfer matrix model (TMM) are outlined in detail. The estimation algorithms are evaluated with Monte Carlo simulations and measurement data from an incoherent optical frequency domain reflectometer (iOFDR). The model-based approach outperforms conventional Fourier transform processing, especially near the spatial resolution limit, saving electrical bandwidth and measurement time. The models provide great flexibility and can be easily expanded in complexity to meet different topologies and to include prior knowledge of the sensors. Systematic errors due to crosstalk between gratings caused by multiple reflections and spectral shadowing could be further considered with the TMM to improve the performance of large-scale FBG array sensor systems.

关键词： optical fiber sensing fiber Bragg gratings signal processing modeling estimation of distribution algorithm optical frequency domain reflectometry temperature sensing

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A Learning Strategy for Multi-robot Based on Probabilistic Evolutionary algorithm

A Learning Strategy for Multi-robot Based on Probabilistic E...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Fan, Jiancong Liang, Yongquan Ruan, Jiuhong Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Shandong Peoples R China Shandong Jiaotong Univ Sci Res Dept Jinan 250023 Peoples R China

ISBN: (纸本)9781424467129

estimation of distribution algorithm (EDA) is a new evolutionary computation method based on probabilistic theory. EDA can select optimal individuals through estimating probability distribution function of a population. The capture problem among multi software robots can be solved by EDA. The capture problem involves that some pursuers pursue several evaders through part of trajectory. The trajectory was produced by the evaders during their two-dimensional random mobility. The pursuers estimate the evaders' mobility functions and adjust their pursuit models to capture the evaders as fast as possible. The probabilistic evolutionary courses of multi-robot experiencing some competitions are analyzed in performances. The analysis shows that capture problem of multi-robot solved by EDA is better than other methods in several aspects.

关键词： evolutionary computation estimation of distribution algorithm multi-robot competition capture course

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Research on gas emission quantity prediction model based on EDA-IGA

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HELIYON 2023年第7期9卷 e17624页

作者： Ji, Peng Shi, Shiliang Shi, Xingyu Hunan Univ Sci & Technol Coll Resource Environm & Safety Engn Xiangtan 411201 Peoples R China Hunan Univ Sci & Technol Prov Key Lab Safe Min Tech Coal Mines Taoyuan Rd Xiangtan 411201 Hunan Peoples R China Changsha Univ Sci & Technol Wanjiali Rd Changsha 410114 Hunan Peoples R China

In order to accurately predict the possible gas emission quantity in coal mines, it is proposed to use the multi-thread calculation of the Immune Genetic algorithm (IGA) and injection of vaccines to improve the accuracy of prediction and combine the estimation of distribution algorithm (EDA) to the distribution probability of excellent populations. Calculating, and selecting excellent populations for iteration, optimize the population generation process of the Immune Genetic algorithm, so that the population quality is continuously optimized and improved, and the optimal solution is obtained, thereby establishing a gas emission quantity prediction model based on the Immune Genetic algorithm and estimation of distribution algorithm. Using the 9136 mining face with gas emission hazards in a coal mine from Shandong Province in China as the prediction object, the absolute gas emission quantity is used to scale the gas emission quantity, and it is found that the model can accurately predict the gas emission quantity, which is consistent with the on-site emission unanimous. In the prediction comparison with IGA, it is found that the accuracy of the prediction results has increased by 9.51%, and the number of iterations to achieve the required goal has been reduced by 67%, indicating that the EDA has a better role in optimizing the population update process such as genetic selection of the IGA. Comparing the prediction results of other models, it is found that the prediction accuracy of the EDA-IGA is 94.93%, which is the highest prediction accuracy, indicating that this prediction model can be used as a new method for the prediction of coal mine gas emission. Accurately predicting the gas emission quantity can provide guidance for safe mining in coal mines. The gas emission quantity can also be used as a safety indicator to reduce the possibility of coal mine accidents, ensure the personal safety of coal miners and reduce economic losses in coal mines.

关键词： Gas emission quantity Immune genetic algorithm estimation of distribution algorithm Prediction

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Maximizing Net Present Value for Resource Constraint Project Scheduling Problems with Payments at Event Occurrences Using Approximate Dynamic Programming

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algorithmS 2024年第5期17卷 180页

作者： Phuntsho, Tshewang Gonsalves, Tad Sophia Univ Dept Informat & Comp Sci Chiyoda Ku Tokyo 1028554 Japan Royal Univ Bhutan Gedu Coll Business Studies POB 21006 Bongo Bhutan

Resource Constraint Project Scheduling Problems with Discounted Cash Flows (RCPSPDC) focuses on maximizing the net present value by summing the discounted cash flows of project activities. An extension of this problem is the Payment at Event Occurrences (PEO) scheme, where the client makes multiple payments to the contractor upon completion of predefined activities, with additional final settlement at project completion. Numerous approximation methods such as metaheuristics have been proposed to solve this NP-hard problem. However, these methods suffer from parameter control and/or the computational cost of correcting infeasible solutions. Alternatively, approximate dynamic programming (ADP) sequentially generates a schedule based on strategies computed via Monte Carlo (MC) simulations. This saves the computations required for solution corrections, but its performance is highly dependent on its strategy. In this study, we propose the hybridization of ADP with three different metaheuristics to take advantage of their combined strengths, resulting in six different models. The estimation of distribution algorithm (EDA) and Ant Colony Optimization (ACO) were used to recommend policies for ADP. A Discrete cCuckoo Search (DCS) further improved the schedules generated by ADP. Our experimental analysis performed on the j30, j60, and j90 datasets of PSPLIB has shown that ADP-DCS is better than ADP alone. Implementing the EDA and ACO as prioritization strategies for Monte Carlo simulations greatly improved the solutions with high statistical significance. In addition, models with the EDA showed better performance than those with ACO and random priority, especially when the number of events increased.

关键词： approximate dynamic programming net present value discounted cash flows Discrete Cuckoo Search estimation of distribution algorithm Ant Colony Optimization Payment at Event Occurrences

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An EDA for Solving Linear Fractional Bilevel Programming Problems

An EDA for Solving Linear Fractional Bilevel Programming Pro...

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2018 International Conference on Information Technology and Management Engineering (ICITME 2018)

作者： Huafei Chen Hecheng Li Jing Huang School of Mathematics and Statistics Qinghai Normal University School of Mathematics and Statistics Sichuan University of Science&Engineering

Heuristic algorithms have attracted great attention in dealing with bilevel programming problems in recent *** this manuscript,a novel approach termed estimation of distribution algorithm(EDA) is developed to solve a special class of bilevel programming problem,the linear fractional bilevel programming *** this method,individuals are encoded with integer strings whose components are the column indices associated with the bases of the *** is created from a distribution via modeling selected superior *** results have illustrated the feasibility and efficiency of the proposed algorithm.

关键词： estimation of distribution algorithm linear fractional bilevel programming problems probability distribution optimal solutions

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