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A hybrid fuzzy inference prediction strategy for dynamic multi-objective optimization

SWARM AND EVOLUTIONARY COMPUTATION 2018年 43卷 147-165页

作者： Chen, Debao Zou, Feng Lu, Renquan Wang, Xude Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

Many real-world multi-objective optimization problems (MOPs) are dynamic in which variables of search space and/or objective space change over time. Hence the optimization algorithms should can quickly and efficiently track the Pareto front in dealing with dynamic environments. In this paper, a hybrid population prediction strategy based on fuzzy inference and one-step prediction (FIOPPS) is presented to extrapolate ahead the trajectory (position and/or orientation) of the new Pareto optimal solution set from the previous Pareto optimal solution sets and ensure the algorithm to respond quickly and effectively when the environment changes thus tracking the changing Pareto front. In our algorithm, the fuzzy inference model based on the Maximum Entropy Principle is extracted automatically from the previously found Pareto optimal solution sets to predict the Pareto solution sets at the beginning of the next time. Moreover, a new one-step prediction model is proposed to improve the prediction accuracy for environmental changes from motion state to static state and vice versa. Furthermore, a new variant of teaching-learning-based optimization algorithm with decomposition is first proposed as the MOEA optimizer for solving dynamic multi-objective optimization problems (DMOPs). In the proposed MOTLBO/D variant, the multi-objective decomposition mechanism is adopted and neighbor strategy is introduced into teaching-learning-based optimization algorithm (TLBO) to maintain the diversity of population and avoid the algorithm trapping into the local areas. Finally, to verify the performance of the proposed methods, ten benchmark test functions are simulated and evaluated. The statistical results indicate that the proposed FIOPPS strategy is promising for dealing with DMOPs.

关键词： dynamic multi-objective optimization Decomposition Teaching learning-based optimization Prediction Fuzzy inference

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A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

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SOFT COMPUTING 2014年第10期18卷 1913-1929页

作者： Liu, Ruochen Chen, Yangyang Ma, Wenping Mu, Caihong Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China

dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments.

关键词： Coevolution dynamic multi-objective optimization Evolutionary algorithms Predictive model

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Plane Separation: A method to solve dynamic multi-objective optimization problems with incorporated preferences

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 110卷 864-875页

作者： Macias-Escobar, Teodoro Cruz-Reyes, Laura Fraire, Hector Dorronsoro, Bernabe Univ Cadiz Dept Comp Engn Puerto Real Spain Technol Inst Ciudad Madero Natl Technol Inst Mexico Madero City Mexico

dynamic optimization multi-objective problems (DMOPs) are characterized by the environmental changes they experiment . For these problems, optimization algorithms have limited time to find accurate results before every change. A common scenario in optimization is the presence of a decision maker (DM), which establishes preferences on the problem being solved. Nowadays, there are few works focused on applying preference incorporation techniques in DMOPs. This work proposes the Plane Separation (PS) method, a novel technique that allows incorporating preferences in the optimization process by splitting the population into multiple planes based on the proximity of solutions to the region of interest (ROI). PS uses those planes to focus the search towards the ROI while maintaining diversity in the solutions set to avoid stagnation in local optima. PS is incorporated into two versions of DNSGA-II, and in a novel dynamic version of GDE3 we propose. These algorithms are also used as low-level heuristics of a hyper-heuristic. All PS-incorporated algorithms were compared against DRNSGA-II, a dynamic version proposal of a single-reference-point technique for solving MOPs under different preferential setups. The results favor the performance of the proposed PS-based algorithms, confirming its feasibility and effectiveness as a technique for incorporating preferences within a dynamic environment. (C) 2019 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Preference incorporation Evolutionary algorithm Hyper-heuristic

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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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Environment Sensitivity-Based Cooperative Co-Evolutionary Algorithms for dynamic multi-objective optimization

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第6期15卷 1877-1890页

作者： Xu, Biao Zhang, Yong Gong, Dunwei Guo, Yinan Rong, Miao China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China Huaibei Normal Univ Sch Math Sci Huaibei 235000 Peoples R China

dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment. Adopting two populations to cooperatively optimize the two subcomponents, two prediction methods, i.e., differential prediction and Cauchy mutation, are then employed respectively to speed up their responses on the change of the environment. Furthermore, two improved dynamic multi-objective optimization algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are proposed by incorporating the above strategy into NSGA-II and multi-objective particle swarm optimization, respectively. The proposed algorithms are compared with three state-of-the-art algorithms by applying to seven benchmark DMOPs. Experimental results reveal that the proposed algorithms significantly outperform the compared algorithms in terms of convergence and distribution on most DMOPs.

关键词： dynamic multi-objective optimization cooperative co-evolution particle swarm evolutionary algorithm

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A knee-guided prediction approach for dynamic multi-objective optimization

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INFORMATION SCIENCES 2020年 509卷 193-209页

作者： Zou, Fei Yen, Gary G. Tang, Lixin Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Liaoning Engn Lab Operat Analyt & Optimizat Smart Minist Educ Shenyang 110819 Liaoning Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Northeastern Univ Inst Ind & Syst Engn Liaoning Key Lab Mfg Syst & Logist Shenyang 110819 Liaoning Peoples R China

Although dynamic multi-objective optimization problems dictate the evolutionary algorithms to quickly track the varying Pareto front when the environmental change occurs, the decision maker in the loop still needs to select a final optimal solution among a large number of candidate solutions before and after the environmental change. Most designs focus on searching for a well-distributed Pareto front which inadvertently demand excessive computational burden during the evolutionary process. In this paper, we propose a novel knee-guided prediction evolutionary algorithm (KPEA) which maintains non-dominated solutions near knee and boundary regions, in order to reduce the burden of maintaining a large and diversified population throughout the evolution process. When a change is detected, this design relocates the knee and boundary solutions based on the movement of the global knee solution in the new environment. In this way, this algorithm incurs a lower computational cost, allowing the evolutionary algorithm to converge quickly. In order to test the performance of the proposed algorithm, five popular dynamic multi-objective evolutionary algorithms (DMOEAs) are compared with KPEA based on two newly proposed metrics. The experimental results validate that the proposed algorithm effectively and efficiently converges to the global knee solution under the changing environments. (C) 2019 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Knee solution Prediction Decision making

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A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model

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INFORMATION SCIENCES 2021年 580卷 331-351页

作者： Wang, Feng Liao, Fanshu Li, Yixuan Wang, Hui Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China

dynamic multi-objective optimization problems (DMOPs), in which the environments change over time, have attracted many researchers' attention in recent years. Since the Pareto set (PS) or the Pareto front (PF) can change over time, how to track the movement of the PS or PF is a challenging problem in DMOPs. Over the past few years, lots of methods have been proposed, and the prediction based strategy has been considered the most effective way to track the new PS. However, the performance of most existing prediction strategies depends greatly on the quantity and quality of the historical information and will deteriorate due to non-linear changes, leading to poor results. In this paper, we propose a new prediction method, named MOEA/D-GMM, which incorporates the Gaussian Mixture Model (GMM) into the MOEA/D framework for the prediction of the new PS when changes occur. Since GMM is a powerful non-linear model to accurately fit various data distributions, it can effectively generate solutions with better quality according to the distributions. In the proposed algorithm, a change type detection strategy is first designed to estimate an approximate PS according to different change types. Then, GMM is employed to make a more accurate prediction by training it with the approximate PS. To overcome the shortcoming of a lack of training solutions for GMM, the Empirical Cumulative Distribution Function (ECDF) method is used to resample more training solutions before GMM training. Experimental results on various benchmark test problems and a classical real-world problem show that, compared with some state-of-the-art dynamic optimization algorithms, MOEA/D-GMM outperforms others in most cases. (c) 2021 Elsevier Inc. All rights reserved.

关键词： dynamic multi-objective optimization Gaussian Mixture Model Change type detection Resampling

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

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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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A Proactive Resource Allocation Algorithm for UAV-Assisted V2X Communication Based on dynamic multi-objective optimization

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IEEE COMMUNICATIONS LETTERS 2024年第12期28卷 2814-2818页

作者： Ma, Mengyu Wang, Chao Li, Zuxing Liu, Fuqiang Tongji Univ Dept Informat & Commun Engn Shanghai 201804 Peoples R China

This letter addresses a proactive resource allocation problem in an unmanned aerial vehicles (UAV)-assisted vehicle-to-everything (V2X) communication network. The problem, which can be formulated from the perspective of dynamic multi-objective optimization (DMO), focuses on the decisions of power control, rate selection, and channel allocation with the aim to balance the decision objectives of heterogeneous V2X links while ensuring the network's quality of service (QoS). We propose a novel algorithm that enables quick decision-making in a dynamic environment, by predicting the future channel state information (CSI) and searching for the solution set of the problem before the environment changes. The effectiveness and advantages of the proposed method are demonstrated by simulation experiments.

关键词： Heuristic algorithms Vehicle-to-everything Fading channels Vehicle dynamics Resource management Autonomous aerial vehicles Reliability optimization Communication networks Signal to noise ratio V2X communication dynamic multi-objective optimization proactive resource allocation

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A Novel Prediction Strategy Based on Change Degree of Decision Variables for dynamic multi-objective optimization

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IEEE ACCESS 2020年 8卷 13362-13374页

作者： Ou, Junwei Xing, Lining Liu, Min Yang, Lihua Cent South Univ Forestry & Technol Sch Transportat & Logist Changsha 410004 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411105 Peoples R China Qingdao Hengxing Univ Sci & Technol Qingdao 266100 Peoples R China

Effectively balancing the convergence and diversity in dynamic environments is a challenging task. In order to handle the issue, this paper proposes a novel prediction strategy based on change degree of decision variables for dynamic multi-objective optimization (CDDV), which has the ability to detect the change degree in the decision space and design the different prediction strategy to make the population adapt to the new environment. The proposed method can adaptively increase population diversity according to the analysis of change degree, when an environmental change is detected. In order to study the efficacy and usefulness of the novel change degree on evolutionary algorithms, a range of dynamic multi-objective benchmark problems are selected to evaluate the performance of the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm in compared with four other state-of-the-art methods.

关键词： dynamic multi-objective optimization evolutionary algorithms change degree diversity

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