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A dynamic multi-objective optimization evolutionary algorithm for complex environmental changes

KNOWLEDGE-BASED SYSTEMS 2021年 216卷 106612-106612页

作者： Liu, Ruochen Yang, Ping Liu, Jiangdi Xidian Univ Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

dynamic multi-objective optimization problems (DMOPs) have attracted more and more research in the field of evolutionary computation community in recent years. Unlike most existing approaches just for solving a single change type, we propose a novel dynamic diversity introduction strategy (DDIS), which aims to solve DMOPs with mixed complex environmental changes. Two types of change intensity are presented to jointly determine the proportion of diversity introduction and whether the change type is drastic or slow, and then the inverse modeling and partial population random initialization are served as diversity introduction strategies to respond to environmental changes respectively. The proposed DDIS is incorporated into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework, called DDIS-MOEA/D. For verifying the performance of DDIS, three different mixed change types are constructed by varying severity or frequency of changes and then the proposed algorithm is tested on GTA benchmark problems under the three dynamic characteristics. Experimental results confirm that the proposed approach can successfully identify different change types and dynamically track and adapt complex environmental changes. (C) 2020 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization dynamic diversity introduction strategy Inverse modeling Change intensity Complex environmental changes

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

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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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The IGD-based prediction strategy for dynamic multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Hu, Yaru Peng, Jiankang Ou, Junwei Li, Yana Zheng, Jinhua Zou, Juan Jiang, Shouyong Yang, Shengxiang Li, Jun Xiangtan Univ Dept Comp Sci Coll Xiangtan 411105 Peoples R China Xiangtan Univ Cyberspace Secur Coll Xiangtan 411105 Peoples R China Cent South Univ Dept Automat Changsha Peoples R China Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England Hunan Inst Engn Xiangtan 411105 Peoples R China

In recent years, an increasing number of prediction-based strategies have shown promising results in handling dynamic multi-objective optimization problems (DMOPs), and prediction models are also considered to be very favorable. Nevertheless, some linear prediction models may not always be effective. In particular, when the motion direction trends of different individuals are not aligned, these models can yield inaccurate prediction results. Inverted generational distance (IGD) is a commonly used metric for evaluating the performance of algorithms. This paper proposes a prediction model based on the IGD metric. Specifically, we assume that the pareto optimal front (POF) of the population at the previous time step is the true POF, and the POF at the current time step is the approximate POF. We cluster the current population with reference to the euclidean distances from uniform points on the true POF to the current POF points, with slight overlap between adjacent clusters, enables a better tradeoff between convergence and diversity in the prediction process. We consider the movement directions of individuals within each cluster separately through different cluster distributions, while balancing the individual movement directions and the overall population movement direction by overlaying cluster coverage areas, thereby helping to avoid the clustered prediction population from getting trapped in local optima. Experimental results and comparisons with other algorithms demonstrate that this strategy exhibits strong competitiveness in handling DMOPs.

关键词： Evolutionary algorithms dynamic multi-objective optimization Prediction strategy Inverted generational distance

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A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 62卷

作者： Li, Sanyi Yang, Shengxiang Wang, Yanfeng Yue, Weichao Qiao, Junfei Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

This paper presents a novel population prediction algorithm based on modular neural network (PA-MNN) for handling dynamic multi-objective optimization. The proposed algorithm consists of three mechanisms. First, we set up a modular neural network (MNN) and train it with historical population information. Some of the initial solutions are generated by the MNN when an environmental change is detected. Second, some solutions are predicted based on forward-looking center points. Finally, some solutions are generated randomly to maintain the diversity. With these mechanisms, when the new environment has been encountered before, initial solutions generated by MNN will have the same distribution characteristics as the final solutions that were obtained in the same environment last time. Because the initialization mechanism based on the MNN does not need the solutions in recent time, the proposed algorithm can also solve dynamic multi-objective optimization problems with a dramatically and irregularly changing Pareto set. The proposed algorithm is tested on a variety of test instances with different dynamic characteristics and difficulties. The comparisons of experimental results with other state-of-the-art algorithms demonstrate that the proposed algorithm is promising for dealing with dynamic multi-objective optimization.

关键词： dynamic multi-objective optimization Population prediction Modular neural network

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A new framework of change response for dynamic multi-objective optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 248卷

作者： Hu, Yaru Zou, Juan Zheng, Jinhua Jiang, Shouyong Yang, Shengxiang Xiangtan Univ Sch Comp Sci & Cyberspace Secur Xiangtan 411105 Peoples R China Cent South Univ Dept Automat Changsha 410083 Peoples R China De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England

Combining response strategies into multi -objective evolutionary algorithms (MOEAs) for dynamic multiobjective optimization problems (DMOPs) is very popular. However, most of them hardly focus on DMOPs via enhancing the operator's searching ability of MOEAs. We present a new framework of change response called MOEA/D-HSS. When a change is detected, MOEA/D-HSS updates and assesses saved historical information, computing the intensity of change on the decision variables and the similarity between the current environment and historical ones. Hybrid search strategies (HSS) adaptively adjust the searching range of the population in each generational cycle based on the knowledge above, which has a great chance of discovering new promising regions. HSS is integrated into the variation operator of MOEA based on decomposition (MOEA/D-DE) to enhance its search ability. We take into account that the historical information may be useless references in the later stage of the evolution. Thus, the frequency of HSS usage is gradually decreased in every time interval to balance the population's convergence and diversity. Experimental results demonstrate that MOEA/S-HSS is very competitive on most benchmark problems compared with other state-of-the-art algorithms.

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

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A prediction strategy based on decision variable analysis for dynamic multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Zheng, Jinhua Zhou, Yubing Zou, Juan Yang, Shengxiang Ou, Junwei Hu, Yaru Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Hunan Peoples R China Hengyang Normal Univ Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Many multi-objective optimization problems in reality are dynamic, requiring the optimization algorithm to quickly track the moving optima after the environment changes. Therefore, response strategies are often used in dynamic multi-objective algorithms to find Pareto optimal. In this paper, we propose a hybrid prediction strategy based on the classification of decision variables, which consists of three steps. After detecting the environment change, the first step is to analyze the influence of each decision variable on individual convergence and distribution in the new environment. The second step is to adopt different prediction methods for different decision variables. Finally, adaptive selection is applied to the solution set generated in the first and second steps, and solutions with good convergence and diversity are selected to make the initial population more adaptable to the new environment. The prediction strategy can help the solution set converge while maintaining its diversity. The experimental results and performance show that the proposed algorithm is capable of significantly improving the dynamic optimization performance compared with five state-of-the-art evolutionary algorithms.

关键词： dynamic multi-objective optimization Evolutionary algorithms Decision Variable Analysis Adaptive Selection Diversity

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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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Scalable benchmarks and performance measures for dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2024年 159卷

作者： Sun, Baiqing Zhang, Changsheng Zhao, Haitong Yu, Zhang Northeastern Univ Dept Software Coll Shenyang 110819 Peoples R China Ningxia Inst Sci & Technol Shizuishan 753000 Ningxia Peoples R China Changshu Inst Technol Sch Comp Sci & Engn Changshu 215500 Peoples R China China Telecom Digital Intelligence Technol Co Ltd Beijing Peoples R China

dynamic multi -objective optimization problems (DMOPs) can be utilized to model certain real -world problems that have a dynamic nature. Algorithms for solving DMOPs can be evaluated and improved by comparing their performance on different benchmarks. However, some existing benchmarks for DMOPs have the limitation of non -uniform weights for decision variables. Additionally, dynamic many -objective optimization problems (DMaOPs) involve more than three objectives, but only a few existing benchmarks can be extended to accommodate DMaOPs. Furthermore, some existing performance measures for DMOPs may not effectively compare the relative performance differences between multiple algorithms or evaluate the search uniformity among different objectives. In this paper, we propose improvements to an existing benchmark for DMOPs by expanding the impact range of decision variables. Moreover, a benchmark framework that can be extended to accommodate DMaOPs is proposed, thus addressing a research gap between the optimization of DMOPs and DMaOPs. Additionally, a set of performance measures for DMOPs are proposed, which can evaluate the relative performance and search uniformity of multi -objective optimization algorithms. By comparing the performance of state-of-the-art and commonly used algorithms on test problems, we can gain a better understanding of the characteristics and strengths and weaknesses of the algorithms and test problems.

关键词： dynamic multi-objective optimization Benchmark Performance measures Scalable test functions

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A framework based on generational and environmental response strategies for dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Li, Qingya Liu, Xiangzhi Wang, Fuqiang Wang, Shuai Zhang, Peng Wu, Xiaoming Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain inspired Intelligent Shenzhen 518055 Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Natl Supercomp Ctr JinanShandong Prov Key Lab Com Jinan Shandong Peoples R China Qilu Univ Technol Shandong Acad Sci Biol Engn Technol Innovat Ctr Shandong Prov Heze Branch Jinan Shandong Peoples R China

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage cannot be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs.

关键词： dynamic multi-objective optimization Novel dynamic framework Center point Genetic algorithm Prediction

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