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Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions

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SWARM AND evolutionary COMPUTATION 2021年 60卷

作者： Li, Fan Gao, Liang Garg, Akhil Shen, Weiming Huang, Shifeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Natl CNC Engn Technol Res Ctr Wuhan 430074 Peoples R China

This paper proposed a surrogate-assisted dominance-based multi-objective evolutionary algorithm to solve multi-objective computationally expensive problems with medium dimensions. Two infill criteria are collaboratively used to select promising individuals for exact evaluations. The convergence-based criterion is used to promote the exploitation of current promising areas. This criterion also considers the dispersion of selected solutions to exploit current non-dominant front. The diversity-based criterion is used to enhance the exploration of the population and enhance the accuracy of surrogate models. The feedback information from the convergence-based criterion is used to adjust the frequency of using the diversity-based criterion in order to reduce the consumed function evaluations. Benchmark functions with dimensions varying from 8 to 30 and a reactive power optimization problem are used to test the proposed algorithm. The experimental results demonstrate that the proposed algorithm significantly outperforms some state-of-the-art evolutionary algorithms on most problems.

关键词： Computationally expensive problem multi-objective evolutionary algorithm Infill criterion Surrogate model

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Ensemble summarization of bio-medical articles integrating clustering and multi-objective evolutionary algorithms

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APPLIED SOFT COMPUTING 2021年 106卷 107347-107347页

作者： Mallick, Chirantana Das, Asit Kumar Ding, Weiping Nayak, Janmenjoy Indian Inst Engn Sci & Technol Sibpur 711103 Howrah India Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Aditya Inst Technol & Management AITAM Tekkali 532201 Andhra Pradesh India

The rapid exponential growth of scientific literature of bio-medicine, genomics, and other bio-sciences increases difficulties to access useful information timely and efficiently. The medical researchers, physicians and clinicians need to extract the gist of the novel articles effectively to gather knowledge and apply it properly for better treatment of the patients. In the paper, the bio-medical articles available in an open access source, PubMed MEDLINE, are considered for generating their extractive summaries using clustering and multi-objective evolutionary algorithm based ensemble summarization technique. Initially, we have extracted only the medical terms of an article and determined the concepts of all medical terms using Unified Medical Language System (UMLS) to represent the article in terms of a set of concepts. Next, extractive summaries of the article have been generated using various popularly used clustering algorithms and different centrality measures. The generated summaries are referred to as the base summaries of the article. Finally, multi-objective evolutionary algorithm is applied on these base summaries for generating an ensemble summary of the given article. The evolutionary algorithm uses two competent objective functions to measure the fitness value of each chromosome in the population. One objective function is the linear combination of jaccard similarity and Word2Vec similarity between the original article and the ensemble summary generated by the chromosome. The other objective function is defined as the information gain of the chromosome about the article by generating the ensemble summary. The best pareto optimal solution of the final population of the evolutionary algorithm provides the ensemble summary of the given article. The proposed ensemble method is compared with some state-of-art methods to demonstrate that it is both effective and statistically significant. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Extractive ensemble summarization Natural language processing multi-objective evolutionary algorithm Centrality measures Clustering Bio-medical informatics

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multi-objective evolutionary for synthetic aperture radar image segmentation with non-local means denoising

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NATURAL COMPUTING 2014年第1期13卷 39-53页

作者： Li, Yangyang Wei, Ying Wang, Yang Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

Synthetic aperture radar (SAR) image segmentation is an important problem of the realm of image segmentation. In this study, a novel SAR image segmentation algorithm using a multi-objective evolutionary algorithm based on decomposition with non-local means denoising (MISD) is proposed. The novelty of MISD lies in the following issues: (1) an effective multi-objective method with decomposition to solve SAR image segmentation;(2) in order to denoise the SAR images and retain the details, we employ non-local means to remove the noise. The multi-objective decomposition method makes MISD have lower computational complexity. In order to evaluate the performance of the new method, we compared the results with three other popular segmentation approaches on four simulated and two real SAR images. In our experiments, the new method can always find better results, which means MISD is a promising SAR image segmentation method.

关键词： multi-objective evolutionary algorithm Clustering SAR image segmentation Non-local means Decomposition approach

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A novel membrane-inspired evolutionary framework for multi-objective multi-task optimization problems

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INFORMATION SCIENCES 2022年 596卷 236-263页

作者： Xu, Zhiwei Zhang, Kai He, Juanjuan Liu, Xiaoming Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Membrane-inspired evolutionary algorithm Membrane computing evolutionary multitasking multi-objective multi-task optimization multi-objective evolutionary algorithm

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An evolutionary multitasking Method for High-Dimensional Receiver Operating Characteristic Convex Hull Maximization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1699-1713页

作者： Cheng, Fan Shu, Shengda Zhang, Lei Tan, Ming Qiu, Jianfeng Anhui Univ Inst Informat Mat Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China

Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionary algorithms (EAs) especially multi-objective evolutionary algorithms (MOEAs) have shown their competitiveness in addressing the problem of ROCCH maximization. Thus, a series of MOEAs with promising performance have been proposed to tackle it. However, designing a MOEA for high-dimensional ROOCH maximization is much more challenging due to the "curse of dimension". To this end, in this paper, an evolutionary multitasking approach (termed as EMT-ROCCH) is proposed, where a low-dimensional ROCCH maximization task T-a is constructed to assist the original high-dimensional task T-o. Specifically, in EMT-ROCCH, a low-dimensional assisted task T-a is firstly created. Then, two populations, P-a and P-o, are used to evolve tasks T-a and T-o, respectively. During the evolution, a knowledge transfer from P-a to P-o is designed to transfer the useful knowledge from P-a to accelerate the convergence of P-o. Moreover, a knowledge transfer from P-o to P-a is developed to utilize the useful knowledge in P-o to repair the individuals in P-a, aiming to avoid P-a being trapped into the local optima. Experiment results on 12 high-dimensional datasets have shown that compared with the state-of-the-arts, the proposed EMT-ROCCH could achieve ROCCH with higher quality.

关键词： evolutionary computation evolutionary multitasking ROC convex hull multi-objective evolutionary algorithm knowledge transfer classification

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Product form evolutionary design system construction based on neural network model and multi-objective optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第5期39卷 7977-7991页

作者： Wu, Yixiang Yiwu Ind & Commercial Coll Yiwu 322000 Zhejiang Peoples R China

The product form evolutionary design based on multi-objective optimization can satisfy the complex emotional needs of consumers for product form, but most relevant literatures mainly focus on single-objective optimization or convert multiple-objective optimization into the single objective by weighting method. In order to explore the optimal product form design, we propose a hybrid product form design method based on back propagation neural networks (BP-NN) and non-dominated sorting genetic algorithm-II (NSGA-II) algorithms from the perspective of multi-objective optimization. First, the product form is deconstructed and encoded by morphological analysis method, and then the semantic difference method is used to enable consumers to evaluate product samples under a series of perceptual image vocabularies. Then, the nonlinear complex functional relation between the consumers' perceptual image and the morphological elements is fitted with the BP-NN. Finally, the trained BP-NN is embedded into the NSGA-II multi-objective evolutionary algorithm to derive the Pareto optimal solution. Based on the hybrid BP-NN and NSGA-II algorithms, a multi-objective optimization based product form evolutionary design system is developed with the electric motorcycle as a case. The system is proved to be feasible and effective, providing theoretical reference and method guidance for the multi-image product form design.

关键词： Morphological analysis method kansei engineering back propagation neural networks multi-objective evolutionary algorithm

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Combining mutual information and stable matching strategy for dynamic evolutionary multi-objective optimization

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ENGINEERING OPTIMIZATION 2018年第9期50卷 1434-1452页

作者： Fu, Xiaogang Sun, Jianyong Shanghai Dianji Univ Sch Elect Engn Shanghai Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China

It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence structure to facilitate population re-initialization in evolutionary search paradigms. The aim of the learning techniques is to discover knowledge from history information, thereby to track the movement of the optimal front quickly through good initialization when a change occurs. In this article, a new learning strategy is proposed, where the main ideas are (1) to use mutual information to identify the relationship between previously found approximated solutions;(2) to use a stable matching mechanism strategy to associate previously found optimal solutions bijectively;and (3) to re-initialize the new population based on a kinematics model. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.

关键词： multi-objective evolutionary algorithm dynamic multiobjective optimization kinematics model mutual information stable matching strategy

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A New Approach Based on a multiobjective evolutionary algorithm for Accurate Control of Flow Rate and Blood Pressure in Cardiac Bioreactors

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CARDIOVASCULAR ENGINEERING AND TECHNOLOGY 2020年第1期11卷 84-95页

作者： Gosselin, Jerome Begin-Drolet, Andre Maciel, Yvan Ruel, Jean Univ Laval Dept Genie Mecan Quebec City PQ G1V 0A6 Canada Pavillon Adrien Pouliot Local 1314-C412245 Quebec City PQ Canada

Purpose-Accurately reproducing physiological and time-varying variables in cardiac bioreactors is a difficult task for conventional control methods. This paper presents a new controller based on a genetic algorithm for the control of a cardiac bioreactor dedicated to the study and conditioning of heart valve substitutes. Methods-A multi-objective genetic algorithm was designed to obtain an accurate simultaneous reproduction of physiological periodic time functions of the three most relevant variables characterizing the blood flow in the aortic valve. These three controlled variables are the flow rate and the pressures upstream and downstream of the aortic valve. Results-Experimental results obtained with this new algorithm showed an accurate dynamic reproduction of these three controlled variables. Moreover, the controller can react and adapt continuously to changes happening over time in the cardiac bioreactor, which is a major advantage when working with living biological valve substitutes. Conclusion-The strong non-linear interaction that exists between the three controlled variables makes it difficult to obtain a precise control of any of these, let alone all three simultaneously. However, the results showed that this new control algorithm can efficiently overcome such difficulties. In the particular field of bioreactors reproducing the cardiovascular environment, such a flexible, versatile and accurate reproduction of these three interdependent controlled variables is unprecedented.

关键词： Genetic algorithm multi-objective evolutionary algorithm Optimization Bioreactor

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Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第1期58卷 37-49页

作者： Cruz-Ramirez, Manuel Hervas-Martinez, Cesar Carlos Fernandez, Juan Briceno, Javier de la Mata, Manuel Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain Univ Hosp Reina Sofia Liver Transplantat Unit Cordoba 14004 Spain

objective: The optimal allocation of organs in liver transplantation is a problem that can be resolved using machine-learning techniques. Classical methods of allocation included the assignment of an organ to the first patient on the waiting list without taking into account the characteristics of the donor and/or recipient. In this study, characteristics of the donor, recipient and transplant organ were used to determine graft survival. We utilised a dataset of liver transplants collected by eleven Spanish hospitals that provides data on the survival of patients three months after their operations. Methods and material: To address the problem of organ allocation, the memetic Pareto evolutionary non-dominated sorting genetic algorithm 2 (MPENSGA2 algorithm), a multi-objective evolutionary algorithm, was used to train radial basis function neural networks, where accuracy was the measure used to evaluate model performance, along with the minimum sensitivity measurement. The neural network models obtained from the Pareto fronts were used to develop a rule-based system. This system will help medical experts allocate organs. Results: The models obtained with the MPENSGA2 algorithm generally yielded competitive results for all performance metrics considered in this work, namely the correct classification rate (C), minimum sensitivity (MS), area under the receiver operating characteristic curve (AUC), root mean squared error (RMSE) and Cohen's kappa (Kappa). In general, the multi-objective evolutionary algorithm demonstrated a better performance than the mono-objective algorithm, especially with regard to the MS extreme of the Pareto front, which yielded the best values of MS (48.98) and AUC (0.5659). The rule-based system efficiently complements the current allocation system (model for end-stage liver disease, MELD) based on the principles of efficiency and equity. This complementary effect occurred in 55% of the cases used in the simulation. The proposed rule-based system

关键词： Making decisions rule-based multi-objective evolutionary algorithm Radial basis function neural networks Liver transplantation Organ allocations

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An evolutionary based dynamic energy management framework for IP-over-DWDM networks

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2014年第2期4卷 94-105页

作者： Chen, Xin Phillips, Chris Queen Mary Univ London Sch Elect Engn & Comp Sci Mile End Rd London E1 4NS England

The energy consumption of the Internet accounts for approximately 1% of the world's total electricity usage, which may become one of the main constraints on its further growth. In response, we propose an evolutionary based dynamic energy management framework that reduces the overall energy consumption without degrading network performance. The main concept is to combine infrastructure sleeping with virtual router migration. During off-peak hours, the virtual routers are moved onto fewer physical platforms and the unused resources are placed in a sleep state to save energy. The sleeping physical platforms are then reawakened during busy periods. In particular, an evolutionary based algorithm called MOEA_VRM is developed to determine where to move the virtual routers in question. The algorithm is then evaluated using a multi-layer fluid flow event-driven simulator to assess its potential. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Energy efficiency IP-over-DWDM core networks multi-objective evolutionary algorithm

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