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An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems

JOURNAL OF COMBINATORIAL optimization 2022年第1期44卷 794-849页

作者： Falahiazar, Alireza Sharifi, Arash Seydi, Vahid Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran

dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selection and adaptation operators. Thus, effective operators should be employed to achieve a robust dynamic optimization algorithm. The algorithm presented in this study is known as spread-based dynamic multi-objective algorithm (SBDMOA) that uses bi-directional mutation and convex crossover operators to exploit and explore the search space. The selection operator of the proposed algorithm is inspired by the spread metric to maximize diversity. When the environment changed, the proposed algorithm removes the dominated solutions and mutated all the non-dominated solutions for adaptation to the new environment. Then the selection operator is used to select desirable solutions from the population of non-dominated and mutated solutions. Generational distance, spread, and hypervolume metrics are employed to evaluate the convergence and diversity of solutions. The overall performance of the proposed algorithm is evaluated and investigated on FDA, DMOP, JY, and the heating optimization problem, by comparing it with the DNSGAII, MOEA/D-SV, DBOEA, KPEA, D-MOPSO, KT-DMOEA, Tr-DMOEA and PBDMO algorithms. Empirical results demonstrate the superiority of the proposed algorithm in comparison to other state-of-the-art algorithms.

关键词： dynamic multi-objective optimization problems Evolutionary algorithm Heating optimization optimization algorithm Spread-based

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Solving dynamic multi-objective optimization problems Using Incremental Support Vector Machine

Solving Dynamic Multi-objective Optimization Problems Using ...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Hu, Weizhen Jiang, Min Gao, Xing Tan, Kay Chen Cheung, Yiu-ming Xiamen Univ Dept Cognit Sci Xiamen 361005 Fujian Peoples R China Xiamen Univ Software Sch Xiamen Fujian Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781728121536

The main feature of the dynamic multi-objective optimization problems (DMOPs) is that optimization objective functions will change with times or environments. One of the promising approaches for solving the DMOPs is reusing the obtained Pareto optimal set (POS) to train prediction models via machine learning approaches. In this paper, we train an Incremental Support Vector Machine (ISVM) classifier with the past POS, and then the solutions of the DMOP we want to solve at the next moment are filtered through the trained ISVM classifier. A high-quality initial population will be generated by the ISVM classifier, and a variety of different types of population-based dynamic multi-objective optimization algorithms can benefit from the population. To verify this idea, we incorporate the proposed approach into three evolutionary algorithms, the multi-objective particle swarm optimization(MOPSO), Nondominated Sorting Genetic Algorithm II (NSGA-II), and the Regularity Model-based multi-objective estimation of distribution algorithm(RE-MEDA). We employ experimentS to test these algorithms, and experimental results show the effectiveness.

关键词： dynamic multi-objective optimization problems Incremental Support Vector Machine Pareto Optimal Set

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Solving dynamic multi-objective optimization problems via Support Vector Machine 10

Solving Dynamic Multi-objective Optimization Problems via Su...

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10th International Conference on Advanced Computational Intelligence (ICACI)

作者： Jiang, Min Hu, Weizhen Qiu, Liming Shi, Minghui Tan, Kay Chen Xiamen Univ Dept Cognit Sci & Technol Xiamen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781538643624

dynamic multi-objective optimization problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based dynamic multi-objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.

关键词： dynamic multi-objective optimization problems Support Vector Machine Pareto Optimal Set

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A Memory-Based NSGA-II Algorithm for dynamic multi-objective optimization problems 19th

A Memory-Based NSGA-II Algorithm for Dynamic Multi-objective...

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19th European Conference on the Applications of Evolutionary Computation (EvoApplications)

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Marmara Univ Dept Comp Engn TR-34722 Istanbul Turkey

ISBN: (纸本)9783319311531

dynamic multi-objective optimization problems (DMOPs) have been rapidly attracting the interest of the research community. Although static multi-objective evolutionary algorithms have been adapted for solving the DMOPs in the literature, some of those extensions may have high running time and may be inefficient for the given set of test cases. In this paper, we present a new hybrid strategy by integrating the memory concept with the NSGA-II algorithm, called the MNSGA-II algorithm. The proposed algorithm utilizes an explicit memory to store a number of non-dominated solutions using a new memory updating technique. The stored solutions are reused in later stages to reinitialize part of the population when an environment change occurs. The performance of the MNSGA-II algorithm is validated using three test functions from a framework proposed in a recent study. The results show that performance of the MNSGA-II algorithm is competitive with the other state-of-the-art algorithms in terms of tracking the true Pareto front and maintaining the diversity.

关键词： NSGA-II dynamic multi-objective optimization problems Memory/search algorithms Hybrid genetic algorithms

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An acceleration-based prediction strategy for dynamic multi-objective optimization

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SOFT COMPUTING 2024年第2期28卷 1215-1228页

作者： Zhang, Junxi Qu, Shiru Zhang, Zhiteng Cheng, Shaokang Li, Mingxing Bi, Yang Northwestern Polytech Univ Sch Automat Youyi West Rd 127 Xian 710072 Shaanxi Peoples R China Xian Aeronaut Univ Sch Elect Engn West Ring Rd 259 Xian 710077 Shaanxi Peoples R China

This paper addresses the problem of dynamic multi- objective optimization problems (DMOPs), by demonstrating new approaches to change detection and change prediction in an evolutionary algorithm framework. Because the objectives of such problems change over time, the Pareto optimal set (PS) and Pareto optimal front (PF) are also dynamic. First, we propose a new change detection method which achieves greater sensitivity by considering changes in both the PS and the PF, unlike most previous approaches. Second, when changes occur, a second-order (acceleration-based) prediction strategy is proposed to predictively reinitialize the population close to the new set of optima. We compare the performance of the proposed algorithm against two other state-of-the-art algorithms from the literature, using ten different dynamic benchmark problems. Experimental results show that the proposed change detection strategy in this paper can not only consider the effect of the optimal individuals but also can consider the effect of their corresponding objective values. Compared with the other two methods, the DMOPs achieved both the ability of precisely predicting the direction of changes and the ability of predicting the future trend of change direction. So, the DMOPs can also converge to the true PF in much less iterations compared with other methods. After multiple experiments, the proposed method outperforms the other algorithms on most of the test problems.

关键词： Index Terms dynamic multi-objective optimization problems Predictions Acceleration Evolutionary algorithm

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Cellular teaching-learning-based optimization approach for dynamic multi-objective problems

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KNOWLEDGE-BASED SYSTEMS 2018年 141卷 148-177页

作者： Birashk, Amin Kordestani, Javidan Kazemi Meybodi, Mohammad Reza Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Soft Comp Lab 424 Hafez Ave Tehran Iran

Many real-world optimization problems involve several conflicting objectives that must be optimized simultaneously. Furthermore, most optimization problems have a dynamic structure and change over time. In addition to trying to establish trade-offs among conflicting objectives and explore a diverse set of solutions on a Pareto-optimal front, a dynamic multi-objective optimization (DMOO) algorithm tries to detect changes and track them, using the knowledge of prior environments to converge to the new Pareto-optimal front more quickly. In this paper, a cellular automata-based approach is first proposed for managing and evaluating solutions during the optimization process. Then, using the above approach and the teaching-learning-based optimization algorithm, two new algorithms are introduced for DMOO problems. The first algorithm works to optimize the objectives all at once in a multi-objective manner, while the second algorithm uses the vector evaluated technique to evolve solutions in collaborative single-objective optimization units, and then analyzes them from a multi-objective perspective. These algorithms have been evaluated and compared with some other DMOO algorithms for some standard benchmark problems. The results indicate their superiority in many of the experiments. (C) 2017 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization problems multi-objective teaching-learning-based optimization TLBO Vector evaluated

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dynamic multi-objective evolutionary algorithms in noisy environments

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INFORMATION SCIENCES 2023年第1期634卷 650-664页

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Fatih Sultan Mehmet Vakif Univ Comp Engn Dept Istanbul Turkiye Marmara Univ Fac Engn Comp Engn Dept Istanbul Turkiye

Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study dynamic multi-objective optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of dynamic multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.

关键词： Change detection dynamic multi-objective optimization problems Noise detection Noisy optimization problems Uncertainty

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Solving dynamic Many-objective TSP using NSGA-III equipped with SVR-RBF Kernel Predictor

Solving Dynamic Many-objective TSP using NSGA-III equipped w...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Gupta, Rashi Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

ISBN: (纸本)9781728183923

dynamic multi-objective TSP (DMTSP) finds extensive applications in scheduling and routing problems. The task is challenging due to the change in problem environment (arrangement and number of cities) after certain time period. To solve this, in this manuscript a new prediction based dynamic multi-objective optimization method termed as dynamic non-dominated sorting genetic algorithm III (DNSGA-III) is proposed. This approach reuses the information obtained from previous Pareto optimal sets (POS) to train prediction models. The prediction has been carried out with SVR-RBF, SVR-Linear, polynomial interpolation and cubic spline based prediction approaches and to determine new solutions that are closer to the reference points. This significantly promote population diversity, along with desired convergence. Performance of the proposed DNSGA-III approach has been validated on four benchmark JY test problems. Further a sixteen cities DMTSP problem with two objective functions is solved using the proposed algorithm.

关键词： TSP dynamic multi-objective optimization problems non-dominated sorting Pareto Optimal Set

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Exploiting characterization of dynamism for enhancing dynamic multi-objective evolutionary algorithms

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APPLIED SOFT COMPUTING 2019年 85卷 105783-000页

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Marmara Univ Fac Engn Comp Engn Dept TR-34722 Istanbul Turkey

Characterization of dynamism is an essential phase for some of the dynamic multi-objective evolutionary algorithms (DMOEAs) in order to improve their performance. Although frequency of change and severity of change are the two main perspectives of characterizing dynamic features of the dynamic multi-objective optimization problems (DMOPs), they do not sufficiently attract attentions of the research community. In this paper, we propose a set of new sensor-based change detection schemes for the DMOPs that significantly outperform the current used change detection schemes. Additionally, a new technique is proposed for detecting the change severity for DMOPs. The experimental evaluation based on different test problems and change severity levels validates performance of our technique. We also propose a novel adaptive algorithm called change-responsive NSGA-II (CR-NSGA-II) algorithm that incorporates the change detection schemes, the technique for change severity and a new response mechanism into the NSGA-II algorithm. Our algorithm demonstrates competitive and significantly better results than the leading DMOEAs on majority of test problems and metrics considered. (C) 2019 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization problems dynamic multi-objective evolutionary algorithms Change detection Characterization of change

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A Type Detection Based dynamic multi-objective Evolutionary Algorithm 21st

A Type Detection Based Dynamic Multi-objective Evolutionary ...

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21st International Conference on the Applications of Evolutionary Computation (EvoApplications)

作者： Sahmoud, Shaaban Topcuoglu, Haluk Rahmi Marmara Univ Comp Engn Dept TR-34722 Istanbul Turkey

ISBN: (纸本)9783319775388

Characterization of dynamism is an important issue for utilizing or tailoring of several dynamic multi-objective evolutionary algorithms (DMOEAs). One such characterization is the change detection, which is based on proposing explicit schemes to detect the points in time when a change occurs. Additionally, detecting severity of change and incorporating with the DMOEAs is another attempt of characterization, where there is only a few related works presented in the literature. In this paper, we propose a type-detection mechanism for dynamic multi-objective optimization problems, which is one of the first attempts that investigate the significance of type detection on the performance of DMOEAs. Additionally, a hybrid technique is proposed which incorporates our type detection mechanism with a given DOMEA. We present an empirical evaluation by using seven test problems from all four types and five performance metrics, which clearly validate the motivation of type detection as well as significance of our hybrid technique.

关键词： dynamic multi-objective optimization problems Non-dominated Sorting Genetic Algorithm (NSGA-II) Type detection dynamic multi-objective Evolutionary Algorithms

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