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Real time eco-driving of high speed trains by simulation-based dynamic multi-objective optimization

SIMULATION MODELLING PRACTICE AND THEORY 2018年 84卷 50-68页

作者： Fernandez-Rodriguez, Adrian Fernandez-Cardador, Antonio Cucala, Asuncion P. Comillas Pontifical Univ ICAI Sch Engn Inst Res Technol 23 Alberto Aguilera St Madrid 28015 Spain

Eco-driving is a traffic operation measure that may lead to important energy savings in high speed railway systems. Eco-driving optimization has been applied offline in the design of commercial services. However, the benefits of the efficient driving can also be applied on-line in the regulation stage to recover train delays or in general, to adapt the driving to the changing conditions in the line. In this paper the train regulation problem is stated as a dynamic multi-objective optimization model to take advantage in real time of accurate results provided by detailed train simulation. If the simulation model is realistic, the railway operator will be confident on the fulfillment of punctuality requirements. The aim of the optimization model is to find the Pareto front of the possible speed profiles and update it during the train travel. It continuously calculates a set of optimal speed profiles and, when necessary, one of them is used to substitute the nominal driving. The new speed profile is energy efficient under the changing conditions of the problem. The dynamic multi-objective optimization algorithms DNSGA-II and DMOPSO combined with a detailed simulation model are applied to solve this problem. The performance of the dynamic algorithms has been analyzed in a case study using real data from a Spanish high speed line. The results show that dynamic algorithms are faster tracking the Pareto front changes than their static versions. In addition, the chosen algorithms have been compared with the typical delay recovery strategy of drivers showing that DMOPSO provides 7.8% of energy savings. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Simulation Delay recovery dynamic multi-objective optimization Eco-driving High speed railway Real-time traffic operation

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dynamic multi-objective optimization arising in iron precipitation of zinc hydrometallurgy

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HYDROMETALLURGY 2017年 173卷 134-148页

作者： Han, Jie Yang, Chunhua Zhou, Xiaojun Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

The additions of oxygen and zinc oxide for the goethite process determine the cost and efficiency of the iron precipitation process. As the two production targets (cost and efficiency) are conflicting and the chemical reaction is a continuous process that changes over time, the amounts of additive need to be dynamically optimized to satisfy the requirement of industrial application. In this paper, a discretization method based on control variables and control intervals is proposed to transform the dynamic optimization problem to a nonlinear mathematical programming problem. Then, a multi-objective optimization approach based on the state transition algorithm and constrained nondominated sorting is proposed to find the Pareto optimal solutions. Finally, an evaluation mechanism is proposed to obtain the best solution for industrial applications. The results from a series of simulation experiments show the effectiveness of the proposed approach, e.g. the daily average additions of oxygen and zinc oxide are decreased by 778.0854 m(3) and 4.9013 t, respectively.

关键词： dynamic multi-objective optimization Discretization method State transition algorithm Iron precipitation

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A feedback-based prediction strategy for dynamic multi-objective evolutionary optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 172卷 114594-114594页

作者： Liang, Zhengping Zou, Ya Zheng, Shunxiang Yang, Shengxiang Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Prediction methods are widely used to solve dynamic multi-objective optimization problems (DMOPs). The key to the success of prediction methods lies in the accurate tracking of the new location of the Pareto set (PS) or Pareto front (PF) in a new environment. To improve the prediction accuracy, this paper proposes a novel feedback-based prediction strategy (FPS), which consists of two feedback mechanisms, namely correction feedback (CF) and effectiveness feedback (EF). CF is used to correct an initial prediction model. When the environment changes, CF constructs a representative individual to reflect the characteristics of the current population. The predicted solution of this individual in the new environment is calculated based on the initial prediction model. Afterward, a step size exploration method based on variable classification is introduced to adaptively correct the prediction model. EF is applied to enhance the effectiveness of re-initialization in two stages. In the first stage, half of the individuals in the population are re-initialized based on the corrected prediction model. In the second stage, EF re-initializes the rest of the individuals in the population using two rounds of roulette method based on the re-initialization effectiveness feedback of the first stage. The proposed FPS is incorporated into a dynamic multi-objective optimization evolutionary algorithm (DMOEA) based on decomposition resulting in a new algorithm denoted as MOEA/D-FPS. MOEA/D-FPS is compared with six state-of-theart DMOEAs on twenty-two different benchmark problems. The experimental results demonstrate the effectiveness and efficacy of MOEA/D-FPS in solving DMOPs.

关键词： dynamic multi-objective optimization Evolutionary algorithm Variable classification Step size exploration Feedback

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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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Constrained dynamic multi-objective evolutionary optimization for operational indices of beneficiation process

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JOURNAL OF INTELLIGENT MANUFACTURING 2019年第7期30卷 2701-2713页

作者： Yang, Cuie Ding, Jinliang Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China

Operational indices optimization of beneficiation process is a dynamic optimization problem in nature. It is difficult to solve because the related dynamic models of operational indices cannot be achieved easily. Focusing on the operational indices optimization under uncertain environments in production process, this paper first formulates a constrained dynamic multi-objective optimization problem based on the collected data, which considers the changing factors in production and the constraints of operational and production indices, and takes the production indices as optimization objectives and the operational indices as decision variables. To solve the established constrained dynamic multi-objective problem, a prediction with modification mechanism based dynamic multi-objective evolutionary optimization algorithm is proposed. The algorithm first divides the population into several sub-populations and then predicts each sub-population center of new environment independently. New population is generated by Gaussian and uniform distribution based on the estimated centers to improve the convergence speed. At the same time, to ensure the population diversity, a modification strategy is adopted to detect which reference point has no individual associated and produces some individuals around it. The proposed algorithm is applied to solve the dynamic operational indices optimization problem and compared with a constrained and a modified unconstrained dynamic multi-objective optimization algorithm. The statistical results demonstrate the efficiency and effectiveness of the proposed algorithm to solve the real-world dynamic operational indices optimization problem.

关键词： Operational indices dynamic multi-objective optimization Data-driven modeling Constrained optimization

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A Hybrid Immigrants Strategy for dynamic multi-objective optimization 10

A Hybrid Immigrants Strategy for Dynamic Multi-objective Opt...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Shi, Lulu Wu, Yan Zhou, Yan Xidian Univ Sch Math & Stat Xian Shaanxi Peoples R China Army Acad Border & Coastal Def Xian Shaanxi Peoples R China

ISBN: (纸本)9781538643624

dynamic multi-objective optimization problems exist in the real world widely, requiring the optimization algorithm that has the ability to trace the Pareto-optimal set as time goes on. They appeal many people's attention and are widely studied. The paper proposes a hybrid immigrants strategy to deal with dynamic multi-objective problems whose changes are undetected. Hybrid immigrants strategy contains two strategies. One is random immigrants strategy which can help to maintain population diversity. Another is information guided immigrants strategy which can improve the convergence speed by creating possible better solutions according to the guiding direction of the centers between current generation and previous one generation. The proposed algorithm is assessed on some different kinds of test instances. Empirical results show the efficiency and outperformance of our algorithm in maintaining diversity and tracking the Pareto optimal fronts.

关键词： dynamic multi-objective optimization hybrid immigrants information guided immigrants undetected changes

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An Evolutionary dynamic multi-objective optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance 8

An Evolutionary Dynamic Multi-objective Optimization Algorit...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Zhou, Jianwei Zou, Juan Yang, Shengxiang Ruan, Gan Ou, Junwei Zheng, Jinhua Xiangtan Univ Sch Informat Engn Xiangtan 411105 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

ISBN: (纸本)9781538692769

dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems. the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments.

关键词： dynamic multi-objective optimization autonomous guidance

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A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第3期261卷 1028-1051页

作者： Liu, Ruochen Li, Jianxia Fan, Jing Mu, Caihong Jiao, Licheng Xidian Univ Int Collaborat Joint Lab Intelligent Percept & Co Minist EducInt Res Ctr Intelligent Percept & Com Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China

In real-world applications, there are many fields involving dynamic multi-objective optimization problems (DMOPs), in which objectives are in conflict with each other and change over time or environments. In this paper, a modified coevolutionary multi-swarm particle swarm optimizer is proposed to solve DMOPs in the rapidly changing environments (denoted as CMPSODMO). A frame of multi-swarm based particle swarm optimization is adopted to optimize the problem in dynamic environments. In CMPSODMO, the number of swarms (PSO) is determined by the number of the objective functions, and all of these swarms utilize an information sharing strategy to evolve cooperatively. Moreover, a new velocity update equation and an effective boundary constraint technique are developed during evolution of each swarm. Then, a similarity detection operator is used to detect whether a change has occurred, followed by a memory based dynamic mechanism to response to the change. The proposed CMPSODMO has been extensively compared with five state-of-the-art algorithms over a test suit of benchmark problems. Experimental results indicate that the proposed algorithm is promising for dealing with the DMOPs in the rapidly changing environments. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multiple objective programming dynamic multi-objective optimization Coevolution multi-swarm Particle swarm optimization Similarity detective

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The effect of diversity maintenance on prediction in dynamic multi-objective optimization

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APPLIED SOFT COMPUTING 2017年 58卷 631-647页

作者： Ruan, Gan Yu, Guo Zheng, Jinhua Zou, Juan Yang, Shengxiang Xiangtan Univ Informat Engn Coll Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan Hunan Peoples R China Hengyang Normal Univ Minist Educ Hengyang Hunan Peoples R China Hengyang Normal Univ Sch Comp Sci & Technol Hengyang Hunan Peoples R China Univ Surrey Comp Sci Guildford GU2 7XH Surrey England De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

There are many dynamic multi-objective optimization problems (DMOPs) in real-life engineering applications whose objectives change over time. After an environmental change occurs, prediction strategies are commonly used in dynamic multi-objective optimization algorithms to find the new Pareto optimal set (POS). Being able to make more accurate prediction means the algorithm requires fewer computational resources to make the population approximate to the Pareto optimal front (POF). This paper proposes a hybrid diversity maintenance method to improve prediction accuracy. The method consists of three steps, which are implemented after an environmental change. The first step, based on the moving direction of the center points, uses the prediction to relocate a number of solutions close to the new Pareto front. On the basis of self-defined minimum and maximum points of the POS in this paper, the second step applies the gradual search to produce some well-distributed solutions in the decision space so as to compensate for the inaccuracy of the first step, simultaneously and further enhancing the convergence and diversity of the population. In the third step, some diverse individuals are randomly generated within the region of next probable POS, which prompts the diversity of the population. Eventually the prediction becomes more accurate as the solutions with good convergence and diversity are selected after the non-dominated sort [1] on the combined solutions generated by the three steps. Compared with three other prediction methods on a series of test instances, our method is very competitive in convergence and diversity as well as the speed at which it responds to environmental changes. (C) 2017 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Evolutionary algorithms Diversity maintenance Prediction

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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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