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Enhancing dynamic multi-objective optimization Using Opposition-based Learning and Simulated Annealing

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2023年第4期32卷 2350037-2350037页

作者： Ilyas, Kiran Younas, Irfan Univ Management & Technol Sch Syst & Technol Lahore 54000 Pakistan Natl Univ Comp & Emerging Sci FAST Sch Comp Lahore 54000 Pakistan

There are many dynamic real-life optimization problems in which objectives increase or decrease over time, which usually leads to variations in the dimensions of a Pareto front. dynamic multi-objective optimization (DyMO) approaches aim to keep track of the updated Pareto front to tackle the changes which are caused by the dynamic environment. However, the current DyMO approaches do not handle dynamic environments effectively. In this study, a new hybrid dynamic two-archive evolutionary algorithm with a newly added simulated annealing and opposition-based learning strategy is proposed. The proposed method helps to preserve solutions with reasonable diversity and improve convergence by searching for promising solutions within acceptable computational time and effort. To evaluate the efficacy of the suggested method, comprehensive experiments using different multi-objective quality measures such as generational distance, and inverted generational distance have been performed on several benchmark problems with varying numbers of objectives over time. The results of the experiments show that the suggested method outperforms the strategies already in use.

关键词： dynamic multi-objective optimization optimization opposition-based learning simulated annealing

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Temporal distribution-based prediction strategy for dynamic multi-objective optimization assisted by GRU neural network

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INFORMATION SCIENCES 2023年第1期649卷

作者： Hou, Xing Ge, Fangzhen Chen, Debao Shen, Longfeng Zou, Feng Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China Anhui Engn Res Ctr Intelligent Comp & Applicat Cog Huaibei 235000 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Anhui Prov Key Lab Intelligent Comp & Applicat Huaibei 235000 Anhui Peoples R China

To solve dynamic multi-objective optimization problems, evolutionary algorithms must be capable of quickly and accurately tracking the changing Pareto front such that they can respond in a timely and effective manner when detecting environmental changes. To address this challenge, we propose a dynamic multi-objective prediction strategy based on the temporal distribution characteristics assisted by a gated recurrent unit (GRU) neural network (GTBP). First, a time series is created using the improved historical centroid information, and the time series distribution information is represented by the temporal distribution characteristics. Subsequently, a GRU neural network is used to maximize the distribution characteristics and minimize the losses to train the network model. Finally, the individuals composed of the estimated manifold and the population centroids predicted by the model are combined with some individuals randomly generated for increasing the population diversity to form the initial population at the next moment. To evaluate the GTBP performance, it was compared with four dynamic multi-objective algorithms for 15 test problems. The experimental results demonstrated that GTBP is competitive for solving dynamic multi-objective optimization problems.

关键词： dynamic multi-objective optimization Evolutionary algorithms Prediction GRU neural networks Time series

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A two-level parallel decomposition-based artificial bee colony method for dynamic multi-objective optimization problems

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Bai, Yuyang Zhang, Changsheng Bai, Weitong Northeastern Univ Dept Software Engn ShenYing St Shenyang 110102 Liaoning Peoples R China Tianjin Agr Bank China Dept R&D HaiTai St Tianjin 300380 Peoples R China

Many real-world multiple-objective optimization problems have objectives that change over time. These multiple-objective optimization problems are called dynamic multiple-objective optimization problems (DMOPs) and have received an increased attention. To track the changing Pareto front in DMOPs, the Pareto front at a certain moment needs to be obtained as efficiently as possible, which is challenging for most of existing methods. To this end, we propose a two-level parallel decomposition-based artificial bee colony method for solving DMOPs. To sufficiently accelerate the process of obtaining the Pareto front, a two-level parallel structure is designed in our method. In the first-level parallel structure, the dynamic multi-objective optimization problem at a certain moment is decomposed into a set of single-objective optimization problems that could be solved in parallel. In the second-level parallel structure, a parallel artificial bee colony algorithm is applied to solve each decomposed single -objective optimization problem. Specially, the parallel bee colony algorithm in our method is improved to support the exchange of information among neighbor problems, which is widely accepted to be effective in improving the efficiency of obtaining optimal solutions. To support the implementation of our improved parallel artificial bee colony algorithm, a two-level shared memory structure is designed. Our proposed method is compared with 4 widely used methods on CEC' 2018 multi-objective optimization benchmarks and two constrained dynamic multi-objective optimization problems. The experimental results show that our method outperforms other compared methods in efficiency while maintaining good scalability and convergence.(c) 2023 Published by Elsevier B.V.

关键词： dynamic multi-objective optimization Artificial bee colony algorithm Decomposition-based strategy Parallel computing technique

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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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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 dynamic interval multi-objective optimization algorithm based on environmental change detection

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INFORMATION SCIENCES 2025年 694卷

作者： Cai, Xingjuan Li, Bohui Wu, Linjie Chang, Teng Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Com Beijing Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3000 Australia

dynamic interval multi-objective optimization problems are a class of optimization problems whose interval parameters change with the environment. However, the existing algorithms fail to fully consider the characteristics of interval parameters and can not accurately assess the severity of environmental changes, resulting in a decline in the effectiveness of the detection mechanism. Therefore, effectively dealing with the inherent uncertainty of interval values becomes an important challenge. To address these problems, this paper proposes a dynamic interval multi- objective optimization algorithm based on environment change detection (IO-ECD). Firstly, a change severity detection operator is designed by using the average overlap degree of individual objective interval to classify different severity of environmental changes. Secondly, this paper uses the local search and the interval prediction mechanism based on feed-forward centroid and special points set to cope with various levels of environmental changes. Finally, inspired by hypervolume contribution and objective value inaccuracy, an interval crowding distance operator is constructed to guide population evolution. The algorithm is compared with six cutting-edge algorithms in eight test cases and a combinatorial optimization scenario. The experimental results show that the algorithm performs exceptionally well in most aspects and has strong competitiveness.

关键词： dynamic multi-objective optimization Interval optimization Change severity detection Change response

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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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A Novel Scalable Framework For Constructing dynamic multi-objective optimization Problems

A Novel Scalable Framework For Constructing Dynamic Multi-ob...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Tan, Qingshan Li, Changhe Xia, Hai Zeng, Sanyou Yang, Shengxiang China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Hubei Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

ISBN: (纸本)9781728183923

Modeling dynamic multi-objective optimization problems (DMOPs) has been one of the most challenging tasks in the field of dynamic evolutionary optimization. Based on the analysis of the existing DMOPs, several features widely existed in real-world applications are not taken into account: different objectives may have different function models and variables to be optimized;and the number of conflicting variables should be independent from the number of objectives;the time-linkage property is not considered. In order to overcome the above issues, a novel framework for constructing DMOPs is proposed, where all objectives can be designed independently, and the number of the conflicting variables can be tuned by users. Moreover, it is easy to add new dynamic features to this framework. Several classical dynamic multi-objective optimization algorithms are tested on four scenarios, results show that these characteristics are challenging for the existing algorithms.

关键词： dynamic multi-objective optimization Benchmark design Time-linkage

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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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A novel predictive method based on key points for dynamic multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期190卷 116127-116127页

作者： Wang, Chunfeng Yen, Gary G. Zou, Fei Xianyang Normal Univ Sch Math & Stat Xianyang 712000 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China

dynamic multi-objective problem is very difficult to be solved because of the variability of the objective function with time. To overcome the difficult caused by such variability, a predictive method utilizing some key points (including polar points and centroid points) is designed, which contains four critical steps. First, the whole population is automatically divided into multiple clusters, which will be used to preserve a good diversity in the process of population evolution. Second, the technique for order of preference by similarity to ideal solution (TOPSIS), a well-regarded multi-attribute decision making strategy, is exploited to improve its convergence speed further. Third, the polar point and centroid point in each cluster are utilized to obtain the initial population by using sequence predictive method when environmental changes are detected. Fourth, to accelerate the convergence speed, the quantitative value for each individual determined in the prediction process is also used in mating selection and environmental selection. The numerical results imply that the new method can deal with the change of environment effectively and track the Pareto optimal front (POF) quickly. Meanwhile, the comparison results with several selected state-of-the-art methods also show that the overall performance of the proposed method is the best on most benchmark problems.

关键词： dynamic multi-objective optimization TOPSIS Clustering strategy Predictive strategy

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