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Leveraging Evolutionary Algorithms for dynamic multi-objective optimization Scheduling of multi-tenant Smart Home Appliances

Leveraging Evolutionary Algorithms for Dynamic Multi-Objecti...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Trabelsi, Walid Azzouz, Radhia Bechikh, Slim Ben Said, Lamjed IBM Corp Cork Ireland Univ Tunis SOIE Lab Tunis Tunisia

ISBN: (纸本)9781509006229

In parallel to optimizing energy consumption within houses, users' comfort is increasingly considered as an essential success criterion for automated smart home solutions. From the user perspective, balancing trade-offs between energy consumption and users' comfort when scheduling home appliances is a challenging task mainly within dynamic context (energy price, budget, user preferences, energy source, etc). To address this challenge, this paper has modeled appliances scheduling as a dynamic constrained multi-objective optimization problem and have leveraged a recently introduced dynamic evolutionary algorithm for the problem resolution. Moreover, there are typically multiple inhabitants in the same home who often share context-aware applications with various individual preferences which are likely to be conflicting. We propose a new comfort function to support multi-user conflictual preferences. Our experimental results have shown that our approach has a confirmed advantage on the user comfort while coping dynamically with the context changes.

关键词： dynamic multi-objective optimization evolutionary algorithms multi-user preferences user comfort smart house

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Cooperative Co-evolutionary Algorithm for dynamic multi-objective optimization Based on Environmental Variable Grouping

Cooperative Co-evolutionary Algorithm for Dynamic Multi-obje...

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7th International Conference on Swarm Intelligence (ICSI)

作者： Xu, Biao Zhang, Yong Gong, Dunwei Rong, Miao China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Peoples R China Huaibei Normal Univ Sch Math & Sci Huaibei 235000 Peoples R China

ISBN: (纸本)9783319410005;9783319409993

This paper presents a cooperative co-evolutionary dynamic multi-objective optimization algorithm, i.e., DNSGAII-CO for solving DMOPs based on environmental variable grouping. In this algorithm, a new method of grouping decision variables is first presented, in which all the decision variables are divided into two subcomponents according to whether they are interrelated with or without environment parameters. Then, when cooperatively optimizing the two subcomponents by using two populations, two prediction methods, i.e., differential prediction and Cauchy mutation, are employed to initialize them, respectively. The proposed algorithm is applied to a benchmark DMOPs, and compared with two state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of convergence and distribution.

关键词： dynamic multi-objective optimization Cooperative co-evolution Decision variable grouping

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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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dynamic multi-objective optimization of Chemical Processes Using Modified BareBones MOPSO Algorithm

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Journal of Donghua University(English Edition) 2014年第2期31卷 184-189页

作者：杜文莉王珊珊陈旭钱锋 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population ***, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.

关键词： dynamic multi-objective optimization bare-bones particle swarm optimization(PSO) algorithm chemical process

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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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dynamic multi-objective service composition based on improved social learning optimization algorithm

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Hai, Yan Xu, Xin Liu, Zhizhong North China Univ Water Resources & Elect Power Sch Informat Engn Zhengzhou 450046 Henan Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

The technique of multi-objective service composition (MOSC) aims to create powerful and large grained services through aggregating some simple services while optimizing some conflicting objectives, which has become a hot topic in fields of service computing, cloud computing, cloud manufacturing, and so on. Due to the dynamic nature of the services and users' objectives, the problem of MOSC is actually an dynamic multi-objective service composition problem (DMOSC). Recently, although some wonderful research has been conducted on multi-objective service composition, few studies have paid attention to the issue of DMOSC. To fill this gap, we propose the problem of DMOSC for the first time and then develop a method for DMOSC based on the improved social learning optimization algorithm (named DMOSCO). First, we construct the mathematical model for the problem of DMOSC considering the dynamics of services and users' objectives. Then, we devise the changes perception strategy and changes response strategy to deal with the changes of candidate services and users' objectives during the service composition process, which is designed to strike a balance between convergence and diversity within improved SLO. Finally, to solve the problem of DMOSC, we improve the SLO algorithm by designing a new observation learning operator and incorporating the changes detection strategy and response strategy. Extensive experimental results have proved the performance superiority of our proposed method.

关键词： Service composition dynamic multi-objective optimization Quality of service Social learning optimization algorithm

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A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

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SOFT COMPUTING 2014年第10期18卷 1913-1929页

作者： Liu, Ruochen Chen, Yangyang Ma, Wenping Mu, Caihong Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments.

关键词： Coevolution dynamic multi-objective optimization Evolutionary algorithms Predictive model

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dynamic multi-modal multi-objective optimization: A Preliminary Study 17th

Dynamic Multi-modal Multi-objective Optimization: A Prelimin...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Peng, Yiming Ishibuchi, Hisao Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031147210;9783031147203

Many real-world multi-modal multi-objective optimization problems are subject to continuously changing environments, which requires the optimizer to track multiple equivalent Pareto sets in the decision space. To the best of our knowledge, this type of optimization problems has not been studied in the literature. To fill the research gap in this area, we provide a preliminary study on dynamic multi-modal multi-objective optimization. We give a formal definition of dynamic multi-modal multi-objective optimization problems and point out some key challenges in solving them. To facilitate algorithm development, we suggest a systematic approach to construct benchmark problems. Furthermore, we provide a feature-rich test suite containing 10 novel dynamic multi-modal multi-objective test problems.

关键词： Evolutionary multi-objective optimization multi-modal multi-objective optimization dynamic multi-objective optimization Benchmark problems

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DPb-MOPSO: A dynamic Pareto bi-level multi-objective Particle Swarm optimization Algorithm

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Aboud, Ahlem Rokbani, Nizar Fdhila, Raja Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Hussain, Amir Alimi, Adel M. Univ Sousse ISITCom Sousse 4011 Tunisia Univ Sfax Natl Engn Sch Sfax ENIS REGIM Lab Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Univ Sousse High Inst Appl Sci & Technol Sousse Sousse Tunisia Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 Taif 21944 Saudi Arabia King Saud Univ Coll Appl Comp Sci Riyadh Saudi Arabia Edinburgh Napier Univ Sch Comp Edinburgh EH10 5DT Scotland Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci Johannesburg South Africa

Particle Swarm optimization (PSO) system based on the distributed architecture over multiple sub -swarms is very efficient for static multi-objective optimization but has not been considered for solving dynamic multi-objective problems (DMOPs). Tracking the most effective solutions over time and ensuring good exploitation and exploration are the main challenges of solving DMOP. This study proposes a dynamic Pareto bi-level multi-objective Particle Swarm optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable multiple sub-swarm' subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes in the objective function due to time-varying parameters. A response strategy consisting in reevaluating all unimproved solutions and replacing them with newly generated ones is also implemented. The DPb-MOPSO system is tested on DMOPs with different types of time-varying Pareto Optimal Set (POS) and Pareto Optimal Front (POF). Inverted generational distance (IGD), mean inverted generational distance (MIGD), hypervolume difference (HVD), Robust IGD (RIGD), and Robust General Distance (RGD) metrics are used to assess the DPb-MOPSO performance. Quantitative results are analyzed using Friedman's analysis of variance, and the Wilcoxon sum ranks test, while the stability is analyzed using Lyapunov's theorem. The DPb-MOPSO is more robust than several dynamic multi -objective evolutionary algorithms in solving 21 complex problems over a range of changes in both the POS and POF. On IGD and HVD, DPb-MOPSO can solve 8/13 and 8/13 of the 13 UDF and ZJZ functions with moderate changes. DPb-MOPSO can resolve 7/8 FDA and DMOP benchmarks with severe changes to the MIGD, and 6/8 with m

关键词： dynamic multi-objective optimization problem dynamic particle swarm optimization Detectors dynamic response Friedman analysis of variance Wilcoxon test Lyapunov theorem

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