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

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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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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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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multiregional co-evolutionary algorithm for dynamic multiobjective optimization

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INFORMATION SCIENCES 2021年 545卷 1-24页

作者： Ma, Xuemin Yang, Jingming Sun, Hao Hu, Ziyu Wei, Lixin Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China

dynamic multiobjective optimization problems (DMOPs) require Evolutionary algorithms (EAs) to track the time-dependent Pareto-optimal front (PF) or Pareto-optimal set (PS), and provide diversified solutions. Thus, a multiregional co-evolutionary dynamic multiobjective optimization algorithm (MRCDMO) is proposed based on the combination of a multiregional prediction strategy (MRP) and a multiregional diversity maintenance mechanism (MRDM). To accurately predict the moving trend of PS, a series of center points in different subregions is used to build a difference model to estimate the new location of center points when an environmental change is detected. To promote the diversity of the population, some diverse individuals are generated within the subregion of the next predicted PS. These two parts of solutions make up the population under a new environment. The performance of our proposed method is validated by comparison with four state-of-the-art EAs on 12 test functions. Experimental results demonstrate that the proposed algorithm can effectively cover the changing PF and efficiently predict the location of the moving PS. (C) 2020 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization multiobjective optimization Evolutionary algorithm Prediction Diversity maintenance

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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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A dynamic multi-objective evolutionary algorithm based on prediction

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第1期10卷 1-15页

作者： Wu, Fei Chen, Jiacheng Wang, Wanliang Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

The dynamic multi-objective optimization problem (DMOP) is a common problem in optimization problems;the main reasons are the objective's conflict and environment changes. In this paper, we provide a prediction approach based on diversity screening and special point prediction (DSSP) to tackle the dynamic optimization issue. First, we introduce a decision variable clustering and screening strategy that clusters the decision space of the non-dominated solution set to find the cluster centroids and then employs a decision variable screening strategy to filter out solutions that have an impact on the distribution of individuals. This approach can broaden the range of dynamic multi-objective optimization algorithms. Second, an approach for predicting special points is suggested. The algorithm's convergence is improved following environmental changes by forecasting the special point tracking Pareto front in the object space. Finally, the forward-looking center points are used to predict the non-dominated solution set and eliminate the useless individuals in the population. The prediction strategy can help the solution set converge while maintaining its diversity, which is compared with the four other state-of-the-art strategies. Our experimental results demonstrate that the proposed algorithm, DSSP, can effectively tackle DMOPs.

关键词： dynamic multi-objective optimization prediction diversity maintenance strategy knee point close-to-boundary point convergence

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Considering spatiotemporal evolutionary information in dynamic multi-objective optimisation

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023年第0期

作者： Fan, Qinqin Jiang, Min Huang, Wentao Jiang, Qingchao Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Xiamen Univ Sch Informat Xiamen Fujian Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China 1550 Haigang Ave Shanghai 201306 Peoples R China

Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi-objective optimisation (DMO), are challenging since the sampling space is time- and space-varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO. In the present study, a sliding-time-window-based population clustering method (SPC) is proposed to effectively solve dynamic multi-objective optimisation problems (DMOPs). In the SPC, the knowledge is provided by saving historical data in the temporal dimension, and a spectral clustering method is used to divide the saved data into multiple neighbourhood subspaces for preserving population diversity in the spatial dimension. The SPC is incorporated into the RM-MEDA and is compared with other recently proposed state-of-the-art dynamic multi-objective evolutionary algorithms (DMOEAs) on 14 DMOPs introduced in IEEE CEC2018. Simulation results demonstrate that the proposed method is capable of enhancing the tracking performance of the RM-MEDA in the dynamically changing environments. Additionally, the SPC is utilised to solve an actual dynamic multi-objective translation control problem of an immersed tunnel element. Results show that the proposed SPC outperforms the knee point-based transfer learning method in terms of both computational cost and tracking performance.

关键词： dynamic multi-objective optimization dynamic optimization evolutionary computation high-performing computing immersed tunnel element

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dynamic multi-objective Home Appliance Scheduling Using Transfer Learning Based Evolutionary Algorithm 6

Dynamic Multi-Objective Home Appliance Scheduling Using Tran...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Zhang, Xi Jin, Yaochu Wu, Ping Liu, Qiqi Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Household Energy Management Systems (HEMS) have garnered significant public attention for their ability to optimize the operation of home appliances. Despite extensive research on optimal home appliance scheduling, these scheduling problems are inherently dynamic multi-objective optimization issues that can be effectively addressed using dynamic multi-objective evolutionary algorithms. This paper proposes a dynamic home appliance scheduling model built under time-of-use electricity tariffs to minimize energy costs and user dissatisfaction, taking into account varying user priorities for each appliance at different times. A hybrid method combining elitism-based transfer learning and diversity maintenance (HETD-RM-MEDA) is utilized to solve the scheduling problem, generating optimal schedules that balance energy costs and user dissatisfaction, and providing optimal on/off states for each appliance under different environments. Simulation studies demonstrate the feasibility and effectiveness of the proposed scheduling framework for home appliances under various scenarios.

关键词： dynamic multi-objective optimization evolutionary algorithm transfer learning home appliance scheduling

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Adaptive Intelligent Supplemental Light optimization based on Fluctuating Electricity Prices

Adaptive Intelligent Supplemental Light Optimization based o...

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IEEE Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS)

作者： Sorensen, Jan C. Reybroeck, Eva Univ Southern Denmark Campusvej 55 DK-5230 Odense M Denmark PCS Ornamental Plant Res Schaessestr 18 B-9070 Destelbergen Belgium

ISBN: (纸本)9798350366235;9798350366242

Growers cannot compromise product quality by saving energy, but increasing electricity prices pose an economic challenge. Greenhouse climate control systems must support advanced strategies to optimize these dynamic conflicting goals, but they do not currently do so. The approach utilizes existing hardware and integrates with local climate data, electricity price forecasts, and outdoor weather forecasts, enables advanced multiobjective optimization of climate parameters without requiring new hardware investments. In December 2022, researchers conducted a greenhouse experiment to evaluate the proposed system. Using dynamic multi-objective optimization, they were able to produce a variety of cultivars and save energy without sacrificing quality.

关键词： genetic algorithm dynamic multi-objective optimization software engineering greenhouse climate control

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