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dynamic multiobjective optimization with varying number of objectives assisted by dynamic principal component analysis

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INFORMATION SCIENCES 2024年 665卷

作者： Zou, Fei Yen, Gary G. Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

dynamic multi-objective optimization problems, which are equipped with the increment or decrement number of time-varying objective functions, have been hardly researched in recent decades. Different from other dynamism handling approaches, we propose a new framework incorporated with the dynamic principal component analysis technique, which embeds the acquired knowledge of Pareto optimal set during the evolutionary search process. As the environmental changes occur, the dynamic principal component analysis technique learns the global structure of Pareto optimal set incrementally as newly generated data are collected to depict the manifold contour. In addition, this method constructs high-quality solutions on the basis of obtained knowledge, which in turn captures the main structure of previous solutions. We undertake comprehensive experiments in which the benchmark instances are given with a varying number of objective functions and the computational values are assessed with respect to performance metrics. The obtained statistical findings with 70% improvement fully demonstrate that our proposed algorithm is efficient and effective for solving dynamic multi-objective optimization problems.

关键词： Changing objectives dynamic multi-objective optimization dynamic principal component analysis Machine learning Evolutionary algorithm

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dynamic Constrained multiobjective optimization Immune Algorithm and Its Application

Dynamic Constrained Multiobjective Optimization Immune Algor...

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7th World Congress on Intelligent Control and Automation

作者： Zhuhong Zhang Chen Shanlong Guizhou Univ Coll Sci Inst Syst Sci & Informat Technol Guiyang 550025 Guizhou Peoples R China

ISBN: (纸本)9781424421138

This work investigates a dynamic constrained multi-objective optimization immune algorithm solving a class of extremely difficult multi-objective optimization problems with constraints and time-variant dimensions of decision space. In designs of the algorithm, several adaptive immune operators relying upon the metaphors of antibody learning are designed to evolve the current antibody population, while an environmental recognition rule, in terms of the function of antibody recognition, is developed to step up the process of optimization for similar or identical environments.. On the other hand, the concept of non-dominance is used to design an antibody evaluation scheme, while the environmental memory set and memory pool as well as the rule of antibody selection are constructed based on the functions of memory and dynamic balance maintenance respectively. Depending on three performance indexes proposed and two popular algorithms compared, numerical experiments show that the proposed algorithm has satisfactory searching effect and the ability of strong environmental tracking.

关键词： dynamic multi-objective optimization Immune algorithm Constraint-handling Environmental tracking

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dynamic multi-objective Estimation of Distribution Algorithm based on Domain Adaptation and Nonparametric Estimation

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INFORMATION SCIENCES 2018年 435卷 203-223页

作者： Jiang, Min Qiu, Liming Huang, Zhongqiang Yen, Gary G. Xiamen Univ Dept Cognit Sci & Technol Xiamen Peoples R China Xiamen Univ Fujian Key Lab Brain Like Comp & Applicat Xiamen Peoples R China Oklahoma State Univ Sch Elect & Comp Engn 202 Engn South Stillwater OK 74078 USA

Although dynamic optimization and multi-objective optimization have made considerable progress individually, solving dynamic multi-objective optimization problems remains a monumental challenge since their multiple, conflicting objectives could change over time. In this paper, we propose a Domain Adaptation and Nonparametric Estimation-based Estimation of Distribution Algorithm, called DANE-EDA, to solve dynamic multi-objective optimization problems. Notable features of the proposed algorithm include the importance sampling, nonparametric density estimation, probabilistic prediction, and a domain adaptation technique seamlessly unified under an innovative framework. The design takes full advantage of the powerful Monte-Carlo method and transfer learning technique. This kind of combination will help the proposed algorithm to maintain a delicate exploration exploitation trade-off from temporal and spatial perspectives. At the same time, it will help the proposed algorithm to overcome the shortcomings caused by transfer learning, specifically, the loss of the diversity. After proving convergence and analyzing the computational complexity of the DANE-EDA, we compare the proposed method with nine EDAs or dynamic multi-objective optimization algorithms on twelve different test instances. The experimental results affirm the effectiveness of the proposed method in addressing dynamic multi-objective optimization problems. (C) 2018 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Domain adaption Nonparametric statistics Importance sampling Transfer learning

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A novel population robustness-based switching response framework for solving dynamic multi-objective problems

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NEUROCOMPUTING 2024年 583卷

作者： Li, Han Fang, Zheng Hu, Liwei Liu, Haonan Wu, Peishu Zeng, Nianyin Xiamen Univ Sch Aerosp Engn Xiamen 361102 Fujian Peoples R China

In this paper, a novel population robustness -based switching response framework (PR-SRF) is proposed to develop effective dynamic multi -objective optimization algorithm (DMOA), which integrates different response strategies to comprehensively cope with the dynamic behaviors. In particular, the population robustness is described by the quantification of how severely the environmental changes affect current population, which is timely graded as three levels of weak, strong, and normal to enable the adaptive switch of three different responses of diversity -enhancement, diversity -maintenance, and the knowledge -transfer, respectively. In this way, associations between the adopted responses and the changing environments are successfully established, thereby facilitating more intelligent decision when handling the dynamic behaviors. According to the benchmark evaluation results, the proposed PR-SRF-DMOA yields better comprehensive performance than several other DMOAs with popular response strategies, and it also outperforms another three DMOAs with hybrid responses, which demonstrates the great competitiveness of our algorithm. In addition, ablation study proves that the proposed PR-SRF can sufficiently exploit the merits of different responses, which effectively alleviates the negative knowledge transfer in extremely fluctuating environments, thereby providing valuable references for the development of evolutionary transfer optimization (ETO) algorithms.

关键词： dynamic multi-objective optimization algorithms (DMOAs) Population robustness Switching response framework Evolutionary transfer optimization (ETO)

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dynamic multi-objective evolution of classifier ensembles for video face recognition

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APPLIED SOFT COMPUTING 2013年第6期13卷 3149-3166页

作者： Connolly, Jean-Francois Granger, Eric Sabourin, Robert Univ Quebec Ecole Technol Super Lab Imagerie Vis & Intelligence Artificielle Montreal PQ H3C 1K3 Canada

Due to a limited control over changing operational conditions and personal physiology, systems used for video-based face recognition are confronted with complex and changing pattern recognition environments. Although a limited amount of reference data is initially available during enrollment, new samples often become available over time, through re-enrollment, post analysis and labeling of operational data, etc. Adaptive multi-classifier systems (AMCSs) are therefore desirable for the design and incremental update of facial models. For real time recognition of individuals appearing in video sequences, facial regions are captured with one or more cameras, and an AMCS must perform fast and efficient matching against the facial model of individual enrolled to the system. In this paper, an incremental learning strategy based on particle swarm optimization (PSO) is proposed to efficiently evolve heterogeneous classifier ensembles in response to new reference data. This strategy is applied to an AMCS where all parameters of a pool of fuzzy ARTMAP (FAM) neural network classifiers (i.e., a swarm of classifiers), each one corresponding to a particle, are co-optimized such that both error rate and network size are minimized. To provide a high level of accuracy over time while minimizing the computational complexity, the AMCS integrates information from multiple diverse classifiers, where learning is guided by an aggregated dynamical niching PSO (ADNPSO) algorithm that optimizes networks according both these objectives. Moreover, pools of FAM networks are evolved to maintain (1) genotype diversity of solutions around local optima in the optimization search space and (2) phenotype diversity in the objective space. Accurate and low cost ensembles are thereby designed by selecting classifiers on the basis of accuracy, and both genotype and phenotype diversity. For proof-of-concept validation, the proposed strategy is compared to AMCSs where incremental learning of FAM networks is

关键词： dynamic multi-objective optimization multi-classifier systems Ensemble methods Incremental learning ARTMAP neural networks Face recognition in video Adaptive biometrics

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A dynamic multi-objective evolutionary algorithm with variable stepsize and dual prediction strategies

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 161卷 390-403页

作者： Peng, Hu Pi, Chen Xiong, Jianpeng Fan, Debin Shen, Fanfan Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiujiang Key Lab Digital Technol Jiujiang 332005 Peoples R China Nanjing Audit Univ Sch Comp Sci Nanjing 211815 Peoples R China

The prediction strategy is a key method for solving dynamic multi-objective optimization problems (DMOPs), particularly the commonly used linear prediction strategy, which has an advantage in solving problems with regular changes. However, using the linear prediction strategy may have limited advantages in addressing problems with complex changes, as it may result in the loss of population diversity. To tackle this issue, this paper proposes a dynamic multi-objective optimization algorithm with variable stepsize and dual prediction strategies (VSDPS), which aims to maintain population diversity while making predictions. When an environmental change is detected, the variable stepsize is first calculated. The stepsize of the nondominated solutions is expressed by the centroid of the population, while the stepsize of the dominated solutions is determined by the centroids of the clustered subpopulations. Then, the dual prediction strategies combine an improved linear prediction strategy with a dynamic particle swarm prediction strategy to track the new Paretooptimal front (PF) or Pareto-optimal set (PS). The improved linear prediction strategy aims to enhance the convergence of the population, while the dynamic particle swarm prediction strategy focuses on preserving the diversity of the population. There have also been some improvements made in the static optimization phase, which are advantageous for both population convergence and diversity. VSDPS is compared with six stateof-the-art dynamic multi-objective evolutionary algorithms (DMOEAs) on 28 test instances. The experimental results demonstrate that VSDPS outperforms the compared algorithms in most instances.

关键词： dynamic multi-objective optimization Variable stepsize Dual prediction strategies

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InDM2: Interactive dynamic multi-objective Decision Making Using Evolutionary Algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 40卷 184-195页

作者： Nebro, Antonio J. Ruiz, Ana B. Barba-Gonzalez, Cristobal Garcia-Nieto, Jose Luque, Mariano Aldana-Montes, Jose F. Univ Malaga Dept Lenguajes & Ciencias Comp Ada Byron Res Bldg E-29071 Malaga Spain Univ Malaga Dept Appl Econ Math Campus El Ejido E-29071 Malaga Spain

dynamic optimization problems involving two or more conflicting objectives appear in many real-world scenarios, and more cases are expected to appear in the near future with the increasing interest in the analysis of streaming data sources in the context of Big Data applications. However, approaches combining dynamic multi objective optimization with preference articulation are still scarce. In this paper, we propose a new dynamic multi-objective optimization algorithm called InDM2 that allows the preferences of the decision maker (DM) to be incorporated into the search process. When solving a dynamic multi-objective optimization problem with InDM2, the DM can not only express her/his preferences by means of one or more reference points (which define the desired region of interest), but these points can be also modified interactively. InDM2 is enhanced with methods to graphically display the different approximations of the region of interest obtained during the optimization process. In this way, the DM is able to inspect and change, in optimization time, the desired region of interest according to the information displayed. We describe the main features of InDM2 and detail how It is implemented. Its performance is illustrated using both synthetic and real-world dynamic multi-objective optimization problems.

关键词： dynamic multi-objective optimization multiple criteria decision making Preferences Evolutionary algorithms jMetalSP

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dynamic multi-objective evolutionary algorithm with center point prediction strategy using ensemble Kalman filter

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SOFT COMPUTING 2021年第7期25卷 5003-5019页

作者： Chen, Min Ma, Yongjie Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Gansu Peoples R China

dynamic multi-objective optimization problems are the multi-objective optimization problems in which the objectives change with environment and time, and the optimization algorithm for solving such problems must be able to track the changed pareto optimal set and further explore the real pareto optimal front. It is a difficult and hot topic in dynamic multi-objective evolutionary algorithm to accurately predict the direction of population movement after environmental changes. In this paper, the ensemble Kalman filter is introduced into dynamic multi-objective optimization problems to predict the population center point after environmental changes. ensemble Kalman filter is a four-dimensional assimilation method that uses Monte Carlo short-term ensemble prediction method to estimate the prediction error covariance and has achieved great success in many fields. In the proposed algorithm, the new population center point is predicted by the ensemble Kalman filter prediction model according to the historical population information when the environment changes, and then the population is reinitialized according to the predicted population center point. The experimental results show that the proposed algorithm is superior to other comparison strategies in most test instances.

关键词： dynamic multi-objective optimization Ensemble Kalman filter Prediction strategy Evolutionary algorithm

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dynamic multi-objective AWPSO in DT-Assisted UAV Cooperative Task Assignment

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第10期41卷 3444-3460页

作者： Deng, Min Yao, Zhiqiang Li, Xingwang Wang, Han Nallanathan, Arumugam Zhang, Zeyang Xiangtan Univ Sch Automat & Elect Informat Hunan Natl Ctr Appl Math Key Lab Credible Intelligent Nav & Positioning Hu Xiangtan 411105 Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454000 Henan Peoples R China Yichun Univ Coll Phys Sci & Engn Yichun 336000 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization objectives. To solve this model, an improved dynamic multi-objective adaptive weighted particle swarm optimization algorithm (DMOAWPSO) is proposed. In the initialization stage, a heuristic method is used to increase the effectiveness of the solution. Besides, the adaptive mutation and subgroup methods are adopted to improve the diversity of the solution. Then, effective environment change detection and response strategies are designed to adapt to dynamic scenarios. Finally, the evaluation metrics are calculated in different instances. Compared with the popular and classic dynamic multi-objective algorithms, the simulation results verify that the proposed algorithm is effective and can cope with the environment changes better in solving the task assignment *** recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization o

关键词： dynamic multi-objective optimization UAV digital twin task assignment evolutionary algorithm

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multi-objective pity beetle algorithm based optimal control of wastewater treatment process

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 170卷 188-206页

作者： Du, Xianjun Peng, Yu Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Lanzhou Univ Technol Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Peoples R China Lanzhou Univ Technol Natl Demonstrat Ctr Expt Elect & Control Engn Educ Lanzhou 730050 Peoples R China Lanzhou Univ Technol 287 Langongping Rd Lanzhou 730050 Gansu Peoples R China

Water is a critically scarce resource for industrial production and social life. The discharge of untreated wastewater into natural waters can pose a serious risk to human health. Most of urban wastewater treatment plants use biochemical methods, the most common of which is the biological reaction through activated sludge to degrade pollutants. Considering the public environmental protection and socio-economic needs, this paper es-tablishes a wastewater treatment process (WWTP) optimization model considering the trade-off between effluent quality (EQ) and energy consumption (EC), and designs an dynamic optimal control framework based on a novel multi-objective evolutionary algorithm (MOEA) to track and control the key variables in the WWTP. The nu-merical experiments of multi-objective test functions show that the proposed MOEA has good convergence and distributivity. Simulation results based on the BSM1 platform show that the constructed framework can accu-rately adjust the set-points of controllers in time to improve the performance, which has broad application prospects in practical applications.

关键词： Evolutionary algorithm dynamic multi-objective optimization Optimal process control Pity beetle algorithm Population prediction Wastewater treatment process

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