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Guided prediction strategy based on regional multi-directional information fusion for dynamic multi-objective optimization

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INFORMATION SCIENCES 2024年 669卷

作者： Feng, Jinyu Chen, Debao Zou, Feng Ge, Fangzhen Bian, Xiaotong Zhang, Xuenan Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Intelligent Comp & Applicat Key Lab Anhui Huaibei 235000 Anhui Peoples R China Suzhou Univ Sch Informat Engn Suzhou 234000 Peoples R China

Region partitioning is effective for solving dynamic multi-objective optimization problems (DMOPs). However, most region partitioning approaches use only specific individual information to predict directions within each region. Their efficiency degrades when the distribution of individuals is irregular, and the use of several methods to obtain high-quality areas incurs high computational costs. To address these problems, this study develops a guided prediction strategy based on regional multi-directional information fusion for dynamic multi-objective optimization (RMDIF). Firstly, quantiles are used in the subregional segmentation, whose computational cost is small. Secondly, to increase the prediction accuracy and adaptability of the algorithm for individuals with irregular distributions, information from the center and boundary points of each subregion is fused to construct a new direction for generating initial individuals in new environments. Similar to the quantile-guided dual-prediction strategy, a dual-space prediction strategy is used to generate individuals in new environments to increase the population diversity. Finally, a "maintain-decline-maintain" strategy is used to determine the proportion of new individuals from two prediction spaces. Compared with the fixed proportion method, the proposed method better balances convergence and diversity. RMDIF and six other algorithms are tested on 27 DMOPs, the proposed algorithm outperformed the others in most cases.

关键词： dynamic multi-objective optimization Regional multi-directional information Prediction Adaptive adjustment

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Knowledge guided Bayesian classification for dynamic multi-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Ye, Yulong Li, Lingjie Lin, Qiuzhen Wong, Ka-Chun Li, Jianqiang Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

dynamic multi-objective optimization problems (DMOPs) typically contain multiple conflicting objectives that vary over time, requiring the optimization algorithms to quickly track the changing Pareto-optimal solutions (POS). Recently, prediction-based dynamic multi-objective evolutionary algorithms (DMOEAs) using transfer learning ideas have been considered promising, which could accelerate the search towards the POS by transferring information from previous environments to estimate the location of the POS in the next environment. However, most of the existing methods only transfer the search experiences from one or two previous environments to construct the prediction model for the next moment, which may ignore the effective information in the earlier search process thereby degrading the prediction accuracy in some cases. In this paper, a knowledge-guided Bayesian classification for DMOEA, called KGB-DMOEA, is proposed, which aims to achieve a robust prediction by fully exploiting the information from all historical environments. Particularly, when an environmental change is detected, a knowledge reconstruction-examination strategy is designed to divide all historical optimal solutions into useful and useless ones, which are used as positive and negative samples, respectively, for training the prediction model in subsequent steps. Then, a non -linear probabilistic classifier, i.e., naive Bayesian classifier, is constructed by using the above training samples, which can fully mine the effective knowledge from all historical environments and predict a high-quality initial population for the new environment. Experimental results on multiple DMOPs test suits demonstrate that KGB-DMOEA is superior to several state-of-the-art DMOEAs in solving various DMOPs. (C) 2022 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Naive Bayesian classification Evolutionary algorithm Cluster algorithm

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Ensemble methods based on characterization of dynamism for dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Jiang, Chuwen Ge, Fangzhen Chen, Debao Liu, Huaiyu Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei Anhui Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol 100Dongshan Rd Huaibei 235000 Peoples R China

dynamic multi-objective optimization problems involve multiple conflicting and time-dependent objectives that change continuously depending on the environment. Therefore, effectively tracking the movement of the Pareto-optimal front and the Pareto-optimal set (PS) using a single strategy is difficult. In this study, we propose an ensemble method called EMCD, which is based on the character-ization of dynamism. EMCD selects different strategies to generate new populations depending on the characteristics of environmental changes. In our proposed algorithm, historical information is used to locate new populations when the current and historical environmental changes are similar. Otherwise, EMCD establishes whether the PS has changed. If the PS changes, individuals from the population of the new environment are generated by using a set of knee points based on the two previous consecutive populations. If not, new populations are generated by using the perturbation-based approach. In addition, we use an adaptive diversity introduction strategy to ensure the solvability of our proposed algorithm in dynamic environments. To verify the effectiveness of EMCD, we compare its performance to that of three state-of-the-art algorithms on fourteen benchmark problems. The experimental results show that EMCD outperforms the comparison algorithms on most of the benchmark problems.(c) 2022 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Evolutionary algorithm Similar environment Memory Prediction Adaptive diversity introduction

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A self-adaptive dynamic multi-objective optimization algorithm based on transfer learning and elitism-based mutation

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NEUROCOMPUTING 2023年 559卷

作者： Zhang, Xi Jin, Yaochu Qian, Feng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Bielefeld Univ Fac Technol Chair Nat Inspired Comp & Engn D-33619 Bielefeld Germany Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England East China Univ Sci & Technol Engn Res Ctr Proc Syst Engn Minist Educ Shanghai 200237 Peoples R China

dynamic multi-objective optimization problems (DMOPs) involve several conflicting objectives, and these objective functions change over time. Therefore, addressing DMOPs necessitates an effective response to environmental changes. However, most existing algorithms only deal with DMOPs with one particular type of environmental changes, whereas real-world dynamic changes are more complicated. Therefore, this paper proposes a self-adaptive DMOEA based on transfer learning and elitism-based mutation (ATM-DMOEA), aiming to efficiently tackle DMOPs exhibiting complex environmental changes. Specifically, a change evaluation method is devised to gauge change intensity and discern whether a change is drastic or gentle. Subsequently, an adaptive change response strategy is implemented to accommodate varying environmental changes. For drastic changes, the algorithm employs an elitism-based manifold transfer learning method, while gentle changes are handled with a diversity enhancement strategy introduced by adaptive elitism-based mutations with a varying mutation probability. The experiments have validated the competitiveness of the proposed ATM-DMOEA on the majority of DMOP test instances with different levels of change severity.

关键词： dynamic multi-objective optimization Self-adaptive change response Transfer learning Diversity introduction

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The real-time dynamic multi-objective optimization of a building integrated photovoltaic thermal (BIPV/T) system enhanced by phase change materials

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JOURNAL OF ENERGY STORAGE 2022年 46卷

作者： Sohani, Ali Dehnavi, Amir Sayyaadi, Hoseyn Hoseinzadeh, Siamak Goodarzi, Erfan Garcia, Davide Astiaso Groppi, Daniele KN Toosi Univ Technol Fac Mech Engn Lab Optimizat Thermal Syst Installat Energy Div POB 19395-199915-19 Pardis StMollasadra Ave Tehran *** Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran Sapienza Univ Rome Dept Planning Design & Technol Architecture Via Flaminia 72 I-00196 Rome Italy

Phase change material (PCM) is employed to enhance the thermal energy storage capacity of a building integrated photovoltaic thermal (BIPV/T) system. For this purpose, the best thickness of PCM is found using the dynamic multi-objective optimization (MOO), in which the performance throughout a year with real-time data is taken into account. In addition to the annual thermal energy storage capacity (AES), the annual energy (electricity) production (AEP), as well as payback period (PBP), the levelized cost (LCOE), and annual carbon-dioxide emission reduction (ACDR) are considered as the objective function to obtain a favorable condition from all the energy, economic, and environmental (3E) perspectives. MOO is done for a residential building in Tehran, Iran, by employing numerical modeling for system simulation and the combination of TOPSIS and NSGA-II techniques to determine the final optimal solution. According to the results, the best PCM thickness is 77.2 mm. Compared to the base condition, in which air is used as the material for energy storage, using PCM with the optimum thickness is accompanied by 22.24%, 9.93%, 17.69%, and 9.59% improvement in AES, AEP, ACDR, and LCOE, respectively. Moreover, PBP in the optimum condition is 3.321 years, which shows that utilizing the results of MOO is economically justifiable.

关键词： Building integrated photovoltaic thermal (BIPV T) system dynamic multi-objective optimization Energy storage Phase change material (PCM) Renewable energy technologies

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A novel combinational response mechanism for dynamic multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期233卷

作者： Aliniya, Zahra Khasteh, Seyed Hossein KN Toosi Univ Technol Dept Comp Engn POB 15875-4416 Tehran Iran

Many real-world multi-objective optimization problems are dynamic. These problems require an optimization algorithm to quickly track optimal solutions after changing the environment. In most dynamic multi-objective optimization algorithms, response mechanisms are used to generate the initial population after the environment changes. In the present study, a novel Combinational Response Mechanism (CRM) is proposed, which consists of three parts. After detecting the environmental change, in the first part, RM-rand, a subpopulation of random solutions is generated using DE/rand/1 operator and Cauchy mutation. The second part, RM-Tr & SP, predicts a subpopulation of solutions using transfer learning (TL) and special points. The third part, RM-M, uses the best solutions of the previous environment with a propagation method based on crowding distance to generate the third subpopulation. A combination of the solutions of these three subpopulations is considered the initial population of the new environment. The proposed response mechanism can converge the set of solutions while maintaining their diversity. Thus, generating solutions with good convergence and diversity makes the initial population more adaptable to the new environment. The examinations were done on 24 common test functions. The experimental results indicate that the performance of the proposed response mechanism in dynamic multi-objective optimization is competitive with five advanced evolutionary algorithms.

关键词： dynamic multi-objective optimization Combinational response mechanism Cauchy mutation Transfer learning Special points Crowding distance

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A pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2019年 85卷 105673-000页

作者： Ou, Junwei Zheng, Jinhua Ruan, Gan Hu, Yaru Zou, Juan Li, Miqing Yang, Shengxiang Tan, Xu Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Informat Engn Coll Minist Educ Xiangtan Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518172 Guangdong Peoples R China Hengyang Normal Univ Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added by another difficulty of tracking the Pareto optimal solutions set (POS) and/or the Pareto optimal front (POF) in dynamic scenarios. Confronting these two issues, this paper proposes a Pareto-based evolutionary algorithm using decomposition and truncation to address such dynamic multi-objective optimization problems (DMOPs). The proposed algorithm includes three contributions: a novel mating selection strategy, an efficient environmental selection technique and an effective dynamic response mechanism. The mating selection considers the decomposition-based method to select two promising mating parents with good diversity and convergence. The environmental selection presents a modified truncation method to preserve good diversity. The dynamic response mechanism is evoked to produce some solutions with good diversity and convergence whenever an environmental change is detected. In the experimental studies, a range of dynamic multi-objective benchmark problems with different characteristics were carried out to evaluate the performance of the proposed method. The experimental results demonstrate that the method is very competitive in terms of convergence and diversity, as well as in response speed to the changes, when compared with six other state-of-the-art methods. (C) 2019 Published by Elsevier B.V.

关键词： dynamic multi-objective optimization Evolutionary algorithms Decomposition Diversity

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Decision-Maker's Preference-Driven dynamic multi-objective optimization

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ALGORITHMS 2023年第11期16卷 504-504页

作者： Adekoya, Adekunle Rotimi Helbig, Marde Stellenbosch Univ Comp Sci Div ZA-7600 Stellenbosch South Africa Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa Griffith Univ Sch ICT Southport 4215 Australia

dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)' preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm's performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM's preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM.

关键词： dynamic multi-objective optimization constrained optimization decision-maker preference differential evolution performance measures

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dynamic multi-objective evolutionary optimization algorithm based on two-stage prediction strategy

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ISA TRANSACTIONS 2023年第1期139卷 308-321页

作者： Guo, Zeyin Wei, Lixin Fan, Rui Sun, Hao Hu, Ziyu Yanshan Univ Engn Res Ctr Minist Educ Intelligent Control Syst & Intelligen Qinhuangdao Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China

Tracking pareto-optimal set or pareto-optimal front in limited time is an important problem of dynamic multi-objective optimization evolutionary algorithms (DMOEAs). However, the current DMOEAs suffer from some deficiencies. In the early optimization process, the algorithms may suffer from random search. In the late optimization process, the knowledge which can accelerate the convergence rate is not fully utilized. To address the above issue, a DMOEA based on the two-stage prediction strategy (TSPS) is proposed. TSPS divides the optimization progress into two stages. At the first stage, multi-region knee points are selected to capture the pareto-optimal front shape, which can accelerate the convergence and maintaining good diversity at the same time. At the second stage, improved inverse modeling is applied to search the representative individuals, which can improve the diversity of the population and is beneficial to predicting the moving location of the pareto-optimal front. Experimental results on dynamic multi-objective optimization test suites show that TSPS is superior to the other six DMOEAs. In addition, the experimental results also show that the proposed method has the ability to respond quickly to environmental changes. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： dynamic multi-objective optimization multi-region knee point Inverse model Prediction strategy

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Handling time-varying constraints and objectives in dynamic evolutionary multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 39卷 222-248页

作者： Azzouz, Radhia Bechikh, Slim Ben Said, Lamjed Trabelsi, Walid Univ Tunis SMART Lab Tunis Tunisia DELL Dublin Ireland

Recently, several researchers within the evolutionary and swarm computing community have been interested in solving dynamic multi-objective problems where the objective functions, the problem's parameters, and/or the constraints may change over time. According to the related literature, most works have focused on the dynamicity of objective functions, which is insufficient since also constraints may change over time along with the objectives. For instance, a feasible solution could become infeasible after a change occurrence, and vice versa. Besides, a non-dominated solution may become dominated, and vice versa. Motivated by these observations, we devote this paper to focus on the dynamicity of both: (1) problem's constraints and (2) objective functions. To achieve our goal, we propose a new self-adaptive penalty function and a new feasibility driven strategy that are embedded within the NSGA-II and that are applied whenever a change is detected. The feasibility driven strategy is able to guide the search towards the new feasible directions according to the environment changes. The empirical results have shown that our proposal is able to handle various challenges raised by the problematic of dynamic constrained multi-objective optimization. Moreover, we have compared our new dynamic constrained NSGA-II version, denoted as DC-MOEA, against two existent dynamic constrained evolutionary algorithms. The obtained results have demonstrated the competitiveness and the superiority of our algorithm on both aspects of convergence and diversity.

关键词： dynamic multi-objective optimization Time-varying constraints Time-dependent objectives Evolutionary algorithms

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