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dynamic multi-objective optimization Framework With Interactive Evolution for Sequential Recommendation

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 1228-1241页

作者： Zhou, Wei Liu, Yong Li, Min Wang, Yu Shen, Zhiqi Feng, Liang Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Nanyang Technol Univ Joint NTU UBC Res Ctr Excellence Act Living Elderl Singapore 639798 Singapore Jing Dong Retail Dept User Growth & Operat Beijing 100176 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

In contrast to traditional recommender systems which usually pay attention to users' general and long-term preferences, sequential recommendation (SR) can model users' dynamic intents based on their behaviour sequences and suggest the next item(s) to them. However, most of existing sequential models learn the ranking score of an item only based on its relevance property, and the personalized user demands in terms of different learning objectives, such as diversity, tail novelty or recency, which have been played essential roles in multi-objective recommendation (MOR), are often neglected in SR. In this paper, we first discuss the importance of considering multiple different objectives within a learning model for recommender system. Next, to capture users' objective-level preferences by utilizing interactive information in the sequential context, we propose a novel dynamic multi-objective Recommendation (DMORec) framework with interactive evolution for SR. In particular, DMORec formulates a dynamic multi-objective optimization task to simultaneously optimize more than two varying objectives in an interactive recommendation process. Moreover, to resolve this optimization task in SR, we develop an evolutionary algorithm with supervised learning approach to obtain the Pareto-optimal solutions of the formulated problem. Comprehensive experiments on four real-world datasets demonstrate the effectiveness of the proposed DMORec for dynamic multi-objective recommendation in sequential recommender systems.

关键词： optimization Behavioral sciences Task analysis Tail Training Recommender systems Tuning dynamic multi-objective optimization multi-objective recommendation sequential recommendation

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dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants

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INFORMATION SCIENCES 2011年第11期181卷 2370-2391页

作者： Huang, Liang Suh, Il Hong Abraham, Ajith Hanyang Univ Intelligence & Commun Robots Lab Dept Comp Sci & Engn Coll Engn Seoul 133791 South Korea SNIRE Machine Intelligence Res Labs MIR Labs Auburn WA 98071 USA

dynamic multi-objective optimization is a current hot topic. This paper discusses several issues that has not been reported in the static multi-objective optimization literature such as the loss of non-dominated solutions, the emergence of the false non-dominated solutions and the necessity for an online decision-making mechanism. Then, a dynamic multi-objective optimization algorithm is developed, which is inspired by membrane computing. A novel membrane control strategy is proposed in this article and is applied to the optimal control of a time-varying unstable plant. Experimental results clearly illustrate that the control strategy based on the dynamic multi-objective optimization algorithm is highly effective with a short rise time and a small overshoot. (C) 2011 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Time-varying system Membrane computing (P systems) Membrane control strategy

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dynamic multi-objective optimization algorithm based on prediction strategy

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JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2018年第2期21卷 411-415页

作者： Li, Er-Chao Ma, Xiang-Qi Lanzhou Univ Technol Fac Elect Engn & Informat Engn Lanzhou 730000 Gansu Peoples R China

In order to effectively solve the dynamic multi-objective optimization problem, a new dynamic multi-objective optimization algorithm based on prediction strategy is provided in this *** algorithm detects changes in the environment by recalculating individuals. At the same time, the prediction model is established based on individuals of the first two generations, which is used to generate the new individual. In order to improve the diversity, Cauchy Mutation is used in the algorithm. Then,there are the non-dominated sort and tournament selection to deal the individual. The proposed algorithm is validated on several typical test functions. Meanwhile, the algorithm is compared to DNSGA-A. The experimental results show that the rate of convergence gets improved and the population solution is closer to the real solution. The algorithm do well in resolving the basic dynamic multi-objective function.

关键词： dynamic multi-objective optimization Prediction model Cauchy mutation

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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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dynamic multi-objective optimization Based on an Improved Lion Group Algorithm 7

Dynamic Multi-objective Optimization Based on an Improved Li...

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7th International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Wang, Yumeng Yu, Xinchang Wang, Jingjing Xu, Huaqiang Shandong Normal Univ Jinan Peoples R China

ISBN: (纸本)9798350385113;9798350385106

In this paper, the conventional lion group algorithm is improved and used to solve dynamic multi-objective optimization Problems (DMOPs). An environment change detection strategy is adopted to determine whether the environment changes by calculating the average difference in fitness values of the hunting lions. Furthermore, the algorithm constructs a time series by recording the positions of the lion king at times t 1 and t to predict the lion king's position at time t + 1 to adapt to the changing environment. The performance of the proposed algorithm is studied by comparing it with four state-ofthe-art dynamic optimization algorithms. The experimental results show that the proposed algorithm outperforms other peer algorithms in most benchmark functions.

关键词： Lion group algorithm dynamic multi-objective optimization time series

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dynamic multi-objective optimization and fuzzy AHP for copper removal process of zinc hydrometallurgy

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Zhou, Xiaojun Sun, Yan Huang, Zhaoke Yang, Chunhua Yen, Gary G. Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

In order to improve the production efficiency and reduce the production cost of copper removal process (CRP), it is necessary to control the addition rate of zinc powder in CRP. In this study, the control of zinc powder addition rate is modeled as a dynamic multi-objective optimization problem (DMOP). A Pareto set which contains multiple control schemes is obtained by using multi-objective state transition algorithm based on decomposition, and then a new fuzzy multi-criteria decision-making (MCDM) is proposed to select the best control scheme. In the MCDM, according to the real-world requirements of CRP, four criteria are adopted to evaluate these schemes, and the fuzzy analytic hierarchy process (AHP) is adopted to determine the criteria weights according to a fuzzy comparison matrix. Furthermore, the nonlinear mixed integer programming model is established to guarantee consistency of the fuzzy comparison matrix. Experimental results of an industrial plant in China have demonstrated the effectiveness and superiority of the proposed method in terms of the cost, reaction rate, stability of outlet copper ion concentration, etc. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Copper removal process Continuous stirred-tank reactor (CSTR) dynamic multi-objective optimization Fuzzy AHP Fuzzy logic

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A novel quantile-guided dual prediction strategies for dynamic multi-objective optimization

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INFORMATION SCIENCES 2021年 579卷 751-775页

作者： Sun, Hao Cao, Anran Hu, Ziyu Li, Xiaxia Zhao, Zhiwei Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligen Qinhuangdao 066004 Hebei Peoples R China Tangshan Univ Dept Comp Sci & Technol Tangshan 063000 Hebei Peoples R China

dynamic multi-objective optimization problems (DMOPs) require evolutionary algorithms (EAs) to accurately track the Pareto-optimal Front (PF) and generate the solutions along the PF in the constantly changing environment. In order to solve the DMOPs, a novel quantileguided dual prediction strategies evolutionary algorithm (NQDPEA) is proposed in this paper. Quantiles are often employed to characterize data in statistics. In NQDPEA, the evolution of the population is guided by the quantile, which is to predict the position of the quantile in a new environment through historical quantile information. Then, a new solution set is expanded according to the location of the new quantile. Moreover, its prediction strategies not only predict Pareto-optimal set (PS) by quantile in the decision space but also predict the PF by quantile in objective space and then mapping back to decision space. Through the adaptive combination strategy, the proportion of the new solutions produced by each prediction strategy changes adaptively. To prove the performance of NQDPEA, it is compared with four powerful EAs on 13 test instances. Experimental results show that NQDPEA can effectively generate high quality solutions uniformly along PF. (c) 2021 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Quantile Dual prediction

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A new dynamic strategy for dynamic multi-objective optimization

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INFORMATION SCIENCES 2020年 529卷 116-131页

作者： Wu, Yan Shi, Lulu Liu, Xiaoxiong Xidian Univ Sch Math & Stat Xian 710071 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

After detecting the change of the environment, it is effective to respond to the change of the environment. However, the majorities of these methods only respond to the change of the environment once, ignoring the use of new more environment information. In this paper, we propose a new algorithm for dynamic multi-objective optimization by combining the evolutionary algorithm and the dynamic strategy. The dynamic strategy consists of two parts which correspond to two responses to the environmental change: restart strategy (RS) and adjustment strategy (AS). RS is to use a small amount of the new environment information and local search to re-initialize the population which is expected to be close to the Pareto solutions in the new environment after the environment change. RS is beneficial for quickly responding to environment changes. AS is to adjust the current population with high quality solutions after getting more accurate environmental information. RS is expected to accelerate the convergence speed of the algorithm. The proposed algorithm is tested on a variety of test instances with different changing dynamics. Experimental results show that the proposed algorithm is very competitive for dynamic multi-objective optimization in comparison with state of-the-art methods. (C) 2020 Elsevier Inc. All rights reserved.

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

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Individual-based self-learning prediction method for dynamic multi-objective optimization

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INFORMATION SCIENCES 2022年 613卷 401-418页

作者： Ou, Junwei Li, Mengjun Xing, Lining Lv, Jimin Hu, Yaru Dong, Nanjiang Zhang, Guoting Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Hunan Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

This paper proposes an individual-based self-learning prediction method for dynamic multi-objective optimization problems, called ISPM, to effectively track the time-varying Pareto-optimal set (POS) in a dynamic environment. The ISPM adjusts the reference points by individual-based self-learning, which differs from existing approaches based on fixed reference points. The self-learning reference points are given according to the information from the previous population to divide the Pareto-optimal front (POF) into the objective space as uniformly as possible. One of the ISPM's advantages is it can improve the influence of the corresponding non-uniform POF for the population's prediction. Furthermore, it is known that each reference point and the original point can form a vector in the objective space. Each vector can present a subregion in the objective space. The moving direction of each subregion in the last two environments is the reference when improving the popula-tion in the new environment to realize local search. Meanwhile, we roughly calculate the change degree of the environment using the self-learning reference point sets at the last two environments to realize the global search. The comprehensive experimental results show that the proposed algorithm can effectively balance convergence and diversity com-pared with other state-of-the-art methods.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithms dynamic multi-objective optimization Prediction method Self-learning reference points

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An adaptive Gaussian process based manifold transfer learning to expensive dynamic multi-objective optimization

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NEUROCOMPUTING 2023年第1期538卷

作者： Zhang, Xi Yu, Guo Jin, Yaochu Qian, Feng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Nanjing Tech Univ Inst Intelligent Mfg Nanjing 211816 Peoples R China Bielefeld Univ Fac Technol Chair Nat Inspired Comp & Engn D-33619 Bielefeld Germany Univ Surrey Dept Comp Sci Guildford GU2 7XH England

Expensive dynamic multi-objective optimization problems (EDMOPs) is one kind of DMOPs where the objectives change over time and the function evaluations commonly involve computationally intensive simulations or costly physical experiments. Hence, the key to solve EDMOPs is to quickly and accurately track the time-varying Pareto optimal fronts under the limit of small number of function evaluations, in which how to augment enough training data to build informative surrogate models and manage the mod-els during the search process. To overcome the issue, we propose a transfer learning based surrogate assisted evolutionary algorithm (TrSA-DMOEA) to efficiently solve EDMOPs. Specifically, when a change occurs, we propose a knee point-based manifold transfer learning method based on geodesic flow kernel, which exploits the knowledge from previous high-quality knee solutions to augment the training data for building Gaussian process models, thereby improving the computational complexity and the quality of solutions. Moreover, to efficiently find the optima with limited budget of function evaluations, a novel surrogate-assisted mechanism based on an adaptive acquisition function is introduced, which achieves a balance between convergence and diversity by adaptively adjusting the weights of the angle -penalized distance and average uncertainty at different search stages. By comparing with state-of-the-art algorithms on widely used test problems, the experimental results demonstrate that the proposed method outperforms others and is able to efficiently solve EDMOPs.(c) 2023 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Transfer learning Gaussian process Surrogate assisted evolutionary algorithm

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