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dynamic multi-objective optimization for mixed traffic flow based on partial least squares prediction model

JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 2019年第13期13卷 -页

作者： Chen, Juan Xue, Zhengxuan Han, Dongxiao Shanghai Univ SHU UTS SILC Business Sch Shanghai 201899 Peoples R China

A dynamic multi-objective genetic algorithm based on partial least squares prediction model (DNSGA-II-PLS) is presented in this paper to solve the mix traffic flow multi-objective timing optimization problem with time-varying traffic demand. Take motor vehicle delay, non-motor vehicle delay, and pedestrian delay as objectives to solve the problem. Make comparison with three improved dynamic multi-objective genetic algorithms based on prediction strategy: dynamic multi-objective evolutionary algorithm based on simple prediction (DNSGA-II-PREM), autonomous regression dynamic multi-objective evolutionary algorithm (DNSGA-II-AR), and mutation-based dynamic multi-objective evolutionary algorithm (DNSGA-II-MUT) under four kinds of test functions. The results show that compared with the other three algorithms, the DNSGA-II-PLS algorithm proposed in this paper shows better performance in convergence and distribution, and this algorithm has less complexity. Finally, taking the intersections of Taiping North Road and Zhujiang Road in Nanjing as the research object, the performance of the algorithm is tested under the simulation environment. The results show that compared with the DNSGA-II-AR, which has best performance among three compared algorithms, the proposed DNSGA-II-PLS algorithm can effectively reduce the delay of motor vehicles by 6.7%, non-motor vehicles by -2.8%, and pedestrian waiting time by 20.5%.

关键词： dynamic multi-objective optimization mixed traffic flow least squares prediction single intersection time-varying traffic demand

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An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems

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COMPLEX & INTELLIGENT SYSTEMS 2023年第1期9卷 659-675页

作者： Chen, Meirong Guo, Yinan Jin, Yaochu Yang, Shengxiang Gong, Dunwei Yu, Zekuan China Univ Min & Technol Sch Math Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England Fudan Univ Huashan Hosp Dept Radiol Shanghai 200040 Peoples R China

In dynamic multi-objective optimization problems, the environmental parameters may change over time, which makes the Pareto fronts shifting. To address the issue, a common idea is to track the moving Pareto front once an environmental change occurs. However, it might be hard to obtain the Pareto optimal solutions if the environment changes rapidly. Moreover, it may be costly to implement a new solution. By contrast, robust Pareto optimization over time provides a novel framework to find the robust solutions whose performance is acceptable for more than one environment, which not only saves the computational costs for tracking solutions, but also minimizes the cost for switching solutions. However, neither of the above two approaches can balance between the quality of the obtained non-dominated solutions and the computation cost. To address this issue, environment-driven hybrid dynamic multi-objective evolutionary optimization method is proposed, aiming to fully use strengths of TMO and RPOOT under various characteristics of environmental changes. Two indexes, i.e., the frequency and intensity of environmental changes, are first defined. Then, a criterion is presented based on the characteristics of dynamic environments and the switching cost of solutions, to select an appropriate optimization method in a given environment. The experimental results on a set of dynamic benchmark functions indicate that the proposed hybrid dynamic multi-objective evolutionary optimization method can choose the most rational method that meets the requirements of decision makers, and balance the convergence and robustness of the obtained non-dominated solutions.

关键词： Changing environment Tracking moving optimum Robust Pareto-optima over time dynamic multi-objective optimization

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Evolutionary dynamic multi-objective optimization algorithm based on Borda count method

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第8期10卷 1931-1959页

作者： Orouskhani, Maysam Teshnehlab, Mohammad Nekoui, Mohammad Ali Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran KN Toosi Univ Dept Elect Engn Ind Control Ctr Excellence Tehran Iran

In this paper, a novel dynamic multi-objective optimization algorithm is introduced. The proposed method is composed of three parts: change detection, response to change, and optimization process. The first step is to use Sentry solutions to detect the environmental change and advises the algorithm when a change occurs. Then, to increase the diversity of solutions, the worst solutions should be elected and removed from population and re-initialized with new solutions. The main idea is to use Borda count method which is an optimal rank aggregation technique that ranks the solutions in order of preference and nominates the worst solutions that should be removed. The last step is optimization process which is done by multi-objective Cat swarm optimization (CSO) in this paper. CSO utilizes the population that has been improved from the previous step to estimate the best solutions and converges to optimal Pareto front. The performance of the proposed algorithm is tested on dynamic multi-objective benchmarks, and the results are compared with the ones achieved by previous algorithms. The simulation results indicate that the proposed algorithm can effectively track the time-varying optimal Pareto front and achieves competitive results in comparison with traditional approaches.

关键词： dynamic multi-objective optimization Borda count ranking method Cat swarm optimization

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Knowledge hierarchy-based dynamic multi-objective optimization method for AUV path planning in cooperative search missions

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Wang, Yinhuan Liu, Kaizhou Geng, Lingbo Zhang, Shaoze Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This study focuses on path planning for Autonomous Underwater Vehicles (AUVs) in underwater cooperative search missions. The complexities of the ocean environment, the uncertainty of target movements, and limited communication capabilities render underwater cooperative search missions dynamic multi-objective optimization challenges. Studies focusing on communication-constrained search strategies still lack reliable metrics for assessing underwater communication quality and do not consider communication dead zones. Addressing these deficiencies, this paper introduces a novel communication optimization strategy utilizing packet error rate as a metric for assessing communication efficacy, complemented by a potential field-based method for navigating out of communication dead zones. To tackle the inherently dynamic nature of cooperative search missions for mobile underwater targets, we propose a dynamic multi-objective optimization algorithm that employs a knowledge hierarchy strategy. This method enhances the NSGA-II algorithm's efficiency by extracting effective gene segments from historical Pareto solution set and generating a new initial population through recombination, hierarchy, and prediction. Distinct from other advanced dynamic multi-objective optimization approaches that are limited to theoretical problems, our approach is directly applicable to practical scenarios. The effectiveness and practicality of the proposed method are validated through a series of simulation experiments considering the impact of underwater acoustic communication. These results demonstrate that this research is not only innovative in theory but also holds significant engineering value and practical prospects in real-world applications.

关键词： Autonomous underwater vehicle (AUV) Cooperative search dynamic multi-objective optimization NSGA-II Restricted communication Underwater acoustic channel model

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A special point-based transfer component analysis for dynamic multi-objective optimization

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COMPLEX & INTELLIGENT SYSTEMS 2023年第2期9卷 1229-1245页

作者： Liu, Ruochen Li, Nanxi Peng, Luyao Wu, Kai Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Ctr Intelligent Percept & Computat Xian 710071 Peoples R China

To solve dynamic multi-objective optimization problems better, the key is to adapt quickly to environmental changes and track the possible changing optimal solutions in time. In this paper, we propose a special point-based transfer component analysis for dynamic multi-objective optimization algorithm (SPTr-RM-MEDA). To be specific, when a change occurs, the neighbors of some special points are selected from the optimal set at previous time, and the transfer component analysis makes the use of minimizing the distance between the mapped previous optima and the mapped current optima. Accordingly, the purpose is to predict a part of next initial population from the neighborhoods of special points by transfer component analysis. To adapt to the change well, SPTr-RM-MEDA also reevaluates the previous optimal set. In addition, an adaptive diversity introduction strategy is adopted to maintain the population size. SPTr-RM-MEDA is performed on 12 test problems under 8 kinds of environmental changes, and experimental results show that it is superior to other five state-of-the-art algorithms on most of test problems.

关键词： Special point Transfer component analysis dynamic multi-objective optimization

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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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An Adaptive Knowledge Transfer Strategy for Evolutionary dynamic multi-objective optimization 18th

An Adaptive Knowledge Transfer Strategy for Evolutionary Dyn...

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18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Zhao, Donghui Lu, Xiaofen Tang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

ISBN: (纸本)9789819722716;9789819722723

dynamic multi-objective optimization problems (DMOPs) are optimization problems involve multiple conflicting objectives, and these objectives change over time. The challenge in solving DMOPs is how to quickly track the Pareto optimal solution set when the environment changes. Recently, dynamic multi-objective evolutionary algorithms (DMOEAs) combined with transfer learning (TL) have been proven to be promising in solving DMOPs. TL-based DMOEAs showed advantages in reusing historical information and predicting high-quality solutions in the new environment. Various TL techniques have been employed to DMOEAs, which learn and transfer knowledge either in decision space or in objective space to predict the Pareto optimal solutions. However, problems usually have different types of change in decision and objective spaces. A single knowledge learning and transfer strategy may be unsuitable for all types of DMOPs. In this paper, a DMOEA with an adaptive knowledge learning and transfer strategy is proposed to solve DMOPs. It first estimates the change type of the problem when the environment changes, i.e., whether there exists change in decision or objective spaces, and then based on the change type, it adaptively chooses to learn and transfer knowledge in the decision space or objective space or both to generate an initial population that guides the search in new environment. A comprehensive empirical study is conducted to evaluate the performance of the proposed method. The method is compared to six state-of-the-art prediction-based DMOEAs on widely used DMOP benchmarks. Experimental results demonstrate that the proposed method outperforms or achieves comparable results to the compared algorithms on most of the test problems.

关键词： dynamic multi-objective optimization Evolutionary algorithm Transfer learning Change type

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Computational Study on Effectiveness of Knowledge Transfer in dynamic multi-objective optimization

Computational Study on Effectiveness of Knowledge Transfer i...

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

作者： Ruan, Gan Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham CERCIA Sch Comp Sci Birmingham W Midlands England Honda Res Inst Europe GmbH D-63073 Offenbach Germany Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781728169293

Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the 'inner' optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.

关键词： Evolutionary Algorithms Transfer Learning dynamic multi-objective optimization Prediction-based Method

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Solving dynamic multi-objective optimization Problems Using Cultural Algorithm based on Decomposition 3

Solving Dynamic Multi-Objective Optimization Problems Using ...

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3rd International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Ravichandran, Ramya Kobti, Ziad Univ Windsor Sch Comp Sci Windsor ON Canada

ISBN: (纸本)9781450376259

The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. As a result, the Pareto optimal solutions (POS) and Pareto front (PF) will also vary with time. The desired algorithm should not only locate the optima but also track the moving optima efficiently. In this paper, we propose a new Cultural Algorithm (CA) based on decomposition (CA/D). The primary objective of the CA/D algorithm is to decompose DMOP into several scalar optimization subproblems and solve simultaneously. The subproblems are optimized utilizing the information shared only by its neighboring problems. The proposed CA/D is evaluated using CEC 2015 optimization benchmark functions. The results show that CA/D outperforms CA, multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), particularly in hybrid and composite benchmark problems.

关键词： dynamic multi-objective optimization Cultural Algorithm Decomposition

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An Iterative Machine Learning Approach to Informative Performance Reporting in dynamic multi-objective optimization

An Iterative Machine Learning Approach to Informative Perfor...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Herring, Daniel Kirley, Michael Yao, Xin Univ Melbourne Melbourne Vic Australia Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9798400701207

dynamic multi-objective optimization problems (DMOPs) can represent formulations of complex realistic scenarios in industrial, logistics and energy domains. Consistent and comparable testing on DMOP benchmarks has seen recent progress in tools for comprehensive and reproducible testing. Ranges of possible dynamic instances of DMOPs can be generated defined by their frequency and severity of change parameters. Here, a combination of machine learning and evolutionary algorithm techniques allow for an efficient determination of algorithm performance limits. An iterative Support Vector Machine model is integrated with the existing dynamic Parameter Testing Platform (DPTP) for selective (rather than exhaustive) evaluation of dynamic instances to inform on algorithm capability.

关键词： dynamic multi-objective optimization dynamic optimization support vector machine machine learning evolutionary algorithm

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