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Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

作者： Ramya Ravichandran Ziad Kobti School of Computer Science University of Windsor Windsor ON Canada

ISBN: (纸本)9781450376259

The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. As a result, the Pareto optimal solutions (POS) and Pareto front (PF) will also vary with time. The desired algorithm should not only locate the optima but also track the moving optima efficiently. In this paper, we propose a new Cultural Algorithm (CA) based on decomposition (CA/D). The primary objective of the CA/D algorithm is to decompose DMOP into several scalar optimization subproblems and solve simultaneously. The subproblems are optimized utilizing the information shared only by its neighboring problems. The proposed CA/D is evaluated using CEC 2015 optimization benchmark functions. The results show that CA/D outperforms CA, multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), particularly in hybrid and composite benchmark problems.

关键词： Cultural Algorithm Decomposition dynamic multi-objective optimization

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Adaptive dynamic environment response based evolutionary algorithm for dynamic multi-objective optimization

Adaptive dynamic environment response based evolutionary alg...

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第34届中国控制与决策会议

作者： Kanrong Liu Jianchang Liu Shubin Tan Fei Li Tianzi Zheng Yuanchao Liu School of Information Science and EngineeringNortheastern University Department of Electrical and Information Engineering Anhui University of Technology Anhui Province Key Laboratory of Special Heavy Load Robot Anhui Unviersity of Technology

In dynamic multi-objective optimization problems(DMOPs),multiple conflicting objectives vary over ***,a core issue for the DMOPs is to respond the dynamic environments,when the environment change has been *** effectively address DMOPs,a novel method,called adaptive dynamic environment response based dynamic multi-objective evolutionary algorithm(ADER-DMOEA),is proposed in this *** proposed algorithm can adaptively respond the dynamic environment based on the detected environment change ***,a novel environment change severity detection mechanism is developed,where C-metric is introduced to measure the performance of different population obtained by different types of response ***,according to the measurement results,the algorithm adaptively adopts different strategies for environment ***,it can ensure that the proposed method has a good initial population for each new *** proposed method is compared with three algorithms,and the experiment results have demonstrated that ADER-DMOEA is competitive in dealing with dynamic environments.

关键词： Adaptive dynamic environment response dynamic multi-objective optimization Prediction Memory Response mechanism C-metric

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A dynamic Evolutionary multi-objective optimization Algorithm Based on Decomposition and Adaptive Diversity Introduction 12

A Dynamic Evolutionary Multi-objective Optimization Algorith...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Min Liu, Yuzhen Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China

ISBN: (纸本)9781509040933

In addition to the need for satisfying several objectives, many problems in the real-world are also time-varying. These dynamic problems need the optimization algorithm to constantly find the time-varying optimal solutions. This paper proposes a dynamic multi-objective evolutionary algorithm based on decomposition and adaptive diversity introduction (denoted by dMOEAD-DI). Specifically, the dMOEAD-DI decomposes a dynamic multi-objective optimization problem into several dynamic single objective optimization subproblems and solves them at the same time. Also, an adaptive diversity introduction (ADI) method is adopted to respond to the environment change, and the scale of diversity introduction can be automatically calculated by the real degree of dynamic change, so as to enable the population adapt to the new environment quickly. The effectiveness of the dMOEAD-DI algorithm is validated against other two well-known algorithms. Simulation results on four benchmark problems demonstrate that the dMOEAD-DI has better convergence and distribution of non-dominated solutions obtained around the changing Pareto optimal front.

关键词： dynamic multi-objective optimization evolutionary algorithm decomposition population diversity

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A feedback-based prediction strategy for dynamic multi-objective evolutionary optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 172卷 114594-114594页

作者： Liang, Zhengping Zou, Ya Zheng, Shunxiang Yang, Shengxiang Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Prediction methods are widely used to solve dynamic multi-objective optimization problems (DMOPs). The key to the success of prediction methods lies in the accurate tracking of the new location of the Pareto set (PS) or Pareto front (PF) in a new environment. To improve the prediction accuracy, this paper proposes a novel feedback-based prediction strategy (FPS), which consists of two feedback mechanisms, namely correction feedback (CF) and effectiveness feedback (EF). CF is used to correct an initial prediction model. When the environment changes, CF constructs a representative individual to reflect the characteristics of the current population. The predicted solution of this individual in the new environment is calculated based on the initial prediction model. Afterward, a step size exploration method based on variable classification is introduced to adaptively correct the prediction model. EF is applied to enhance the effectiveness of re-initialization in two stages. In the first stage, half of the individuals in the population are re-initialized based on the corrected prediction model. In the second stage, EF re-initializes the rest of the individuals in the population using two rounds of roulette method based on the re-initialization effectiveness feedback of the first stage. The proposed FPS is incorporated into a dynamic multi-objective optimization evolutionary algorithm (DMOEA) based on decomposition resulting in a new algorithm denoted as MOEA/D-FPS. MOEA/D-FPS is compared with six state-of-theart DMOEAs on twenty-two different benchmark problems. The experimental results demonstrate the effectiveness and efficacy of MOEA/D-FPS in solving DMOPs.

关键词： dynamic multi-objective optimization Evolutionary algorithm Variable classification Step size exploration Feedback

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dynamic adaptive multi-objective optimization algorithm based on type detection

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INFORMATION SCIENCES 2024年 654卷

作者： Cai, Xingjuan Wu, Linjie Zhao, Tianhao Wu, Di Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne 3000 Australia

dynamic multi-objective optimization problems (DMOPs) are multiobjective problems that are influenced by dynamically changing environmental parameters. Most current algorithms for solving DMOPs only respond to dynamic changes in the decision space or objective space and also ignore the impact of the type of DMOPs on the algorithm. The changes in the Paretooptimal solution (POS) and Pareto-optimal front (POF) may affect the type of change in DMOPs. Therefore, this paper proposed an adaptive dynamic multi-objective evolutionary algorithm for type detection (TDA-DMOEA). First, the dynamic detection operator is designed to identify the types of dynamic problems. The Wilcoxon signed-rank test and Hyper Volume (HV) are used to detect the difference of POS and POF in two adjacent environments respectively. Then, different response strategies are designed to cope with different types of changes in DMOP. In particular, a multi-angle-based transfer learning method (MA-TL) with a closed kernel function is derived when faced with simultaneous changes in POS and POF. Finally, a comprehensive study of the commonly used benchmark set of DMOPs is presented, and the proposed algorithm achieves better performance in optimizing DMOPs.

关键词： Adaptive response strategy Type detection dynamic multi-objective optimization Transfer learning

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dynamic multiobjective optimization via an improved r-dominance relation and a novel prediction approach

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Hu, Yaru Ou, Junwei Wang, Huibing Zou, Juan Zheng, Jinhua Yang, Shengxiang Xiangtan Univ Dept Comp Coll Xiangtan 411105 Peoples R China Xiangtan Univ Cyberspace Secur Coll Xiangtan 411105 Peoples R China De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England

When decision makers are only interested in a portion of the Pareto optimal front (PF) in dynamic multiobjective optimization problems (DMOPs), dynamic multiobjective evolutionary algorithms (DMOEAs) need to search only for the PF portion of interest to decision makers. However, this is challenging for most existing DMOEAs, as they are designed to search the entire PF, overlooking the preferences of decision makers. Therefore, we present a novel dynamic multiobjective optimization algorithm based on decision makers' preference information, involving an improved r-dominance relation and a response strategy. It focuses on searching for the preference solutions (the region of interest) according to the decision makers' preference information in dynamic multiobjective optimization. The improved r-dominance relation adapts the angle to measure the closeness between the solution and the preference information, which solves the convergence problem of the r-dominance relation since the original r-dominance relation has difficulty converging to the true PF when the preference information is located in the feasible objective region. The prediction mechanism is based on the movement of the population's special points;it helps the population make adjustments in its moving direction and step size towards the new PF when a change is detected. Experimental results show that the proposed algorithm is efficient for dynamic multiobjective optimization and is competitive compared to state-of-the-art methods.

关键词： Preference information dynamic multi-objective optimization Decision makers

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Ant Colony optimization for Simulated dynamic multi-objective Railway Junction Rescheduling

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2017年第11期18卷 2980-2992页

作者： Eaton, Jayne Yang, Shengxiang Gongora, Mario De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Minimizing the ongoing impact of train delays has benefits to both the users of the railway system and the railway stakeholders. However, the efficient rescheduling of trains after a perturbation is a complex real-world problem. The complexity is compounded by the fact that the problem may be both dynamic and multi-objective. The aim of this research is to investigate the ability of ant colony optimization algorithms to solve a simulated dynamic multi-objective railway rescheduling problem and, in the process, to attempt to identify the features of the algorithms that enable them to cope with a multi-objective problem that is also dynamic. Results showed that, when the changes in the problem are large and frequent, retaining the archive of non-dominated solution between changes and updating the pheromones to reflect the new environment play an important role in enabling the algorithms to perform well on this dynamic multi-objective railway rescheduling problem.

关键词： Train rescheduling dynamic multi-objective optimization ant colony optimisation rail transportation UK railway network

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multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging

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ENERGY 2017年 135卷 101-111页

作者： Ma, Haiping Yang, Zhile You, Pengcheng Fei, Minrui Shaoxing Univ Dept Elect Engn Shaoxing Zhejiang Peoples R China Zhejiang Univ Dept Control State Key Lab Ind Control Technol Hangzhou Zhejiang Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

The climate change is addressing unprecedented pressures on conventional power system regarding the significant fossil fuel consumptions and carbon emissions, which largely challenges the conventional power system operation. This paper proposes a novel dynamic non-dominated sorting multi-objective biogeography-based optimization (Dy-NSBBO) to solve multi-objective dynamic economic emission load dispatch considering the mass integration of plug-in electric vehicles (PEVs), namely MO-DEELDP problem. First, a real-world economic emission load dispatch considering PEVs charging is first formulated as a constrained dynamic multi-objective optimization problem. Then a new multi-objective BBO is proposed adopting the non-dominated solution sorting technique, change detection and memory-based selection strategies in the multi-objective BBO method to strengthen the dynamic optimization performance. The proposed Dy-NSBBO is applied to solve three different dynamic economic emission load dispatch cases integrating four plug-in electric vehicle charging scenarios respectively. Comprehensive analysis shows that the novel algorithm is promising to bring considerable economic and environmental benefits to the power system operators and provides competitive charging strategies for policy makers and PEV5 aggregators. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Economic emission load dispatch Plug-in electric vehicles dynamic multi-objective optimization Biogeography-based optimization Non-dominated sorting

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multi-objective Robust optimization Over Time for dynamic Disassembly Sequence Planning

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2024年第1期25卷 111-130页

作者： Zhang, Xin Fang, Yilin Liu, Quan Yazdani, Danial Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

Disassembly sequence planning aims to optimize disassembly sequences of end-of-life (EOL) products in order to minimize the cost and environmental pollutant emission. Various unpredictable factors in the disassembly environment (e.g., EOL product status and capabilities of operators) lead to significant uncertainty making the optimal disassembly sequence change over time. Considering existing multiple objectives and dynamic environment, this problem is indeed dynamic multi-objective optimization. As deploying a new solution (i.e., disassembly sequence) is costly in this problem, it is undesirable to change the deployed solution after each environmental change. In this paper, we first propose a model for disassembly sequence planning problem in which several factors including the environmental changes, deployed solution switching cost, constraints, and multiple objectives are taken into account. To solve this problem where frequently changing the deployed solution must be avoided, we propose a new multi-objective robust optimization over time (ROOT) framework to find robust solutions based on two new robustness definitions: average performance and stability. The proposed framework benefits from a novel accurate online predictor and the knee-oriented dominance which is applied to select the naturally preferred tradeoff solution to meet the application requirements of ROOT. Computational experiments demonstrate the effectiveness of the proposed ROOT framework.

关键词： Robust optimization over time dynamic multi-objective optimization Disassembly sequence planning Knee-oriented dominance

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multi-objective optimization of dynamic Communication Network for multi-UAVs System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第3期73卷 4081-4094页

作者： Zhang, Chen Yao, Wen Zuo, Yuan Gui, Jianjun Zhang, Chuanfu Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou 510275 Peoples R China Chinese Acad Mil Sci Def Innovat Inst Beijing 100071 Peoples R China Chinese Acad Mil Sci Intelligent Game & Decis Lab Beijing 100071 Peoples R China Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou 510275 Peoples R China

In multi-UAVs system, high-quality communication system is the key to ensure cooperation. Most of the existing methods focus on the trade-off between connectivity and the number of network edges. However, for a multi-UAVs communication network with high quality of service (QoS), more metrics of communication network need to be considered, such as the hops and the maximum number of links. In this article, we propose a multi-objective optimization method for dynamic communication network. Firstly, in order to accurately estimate the moved positions with random noise, Long and Short Term Memory network (LSTM) is used to predict succeeding positions of all UAVs. Secondly, Deep Q-network (DQN) with random action space is designed to adaptively adjust the communication network. In particular, it can be applied to large-scale communication networks. Furthermore, we propose a transfer learning method based on the trained LSTM and DQN. In this transfer learning, these two models which are well-trained in smaller communication network and combined with principal component analysis (PCA) can be directly used for communication network optimization of larger networks. Experimental results show that our method is not only superior to other methods but also faster in convergence.

关键词： Deep reinforcement learning dynamic multi-objective optimization multi-UAVs communication network transfer learning

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