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Yaochu Jin - One of the best experts on this subject based on the ideXlab platform.

  • An Adaptive Reference Vector-Guided Evolutionary Algorithm Using Growing Neural Gas for Many-Objective Optimization of Irregular Problems.
    IEEE transactions on cybernetics, 2020
    Co-Authors: Qiqi Liu, Yaochu Jin, Martin Heiderich, Tobias Rodemann
    Abstract:

    Most Reference Vector-based decomposition algorithms for solving multiobjective optimization problems may not be well suited for solving problems with irregular Pareto fronts (PFs) because the distribution of predefined Reference Vectors may not match well with the distribution of the Pareto-optimal solutions. Thus, the adaptation of the Reference Vectors is an intuitive way for decomposition-based algorithms to deal with irregular PFs. However, most existing methods frequently change the Reference Vectors based on the activeness of the Reference Vectors within specific generations, slowing down the convergence of the search process. To address this issue, we propose a new method to learn the distribution of the Reference Vectors using the growing neural gas (GNG) network to achieve automatic yet stable adaptation. To this end, an improved GNG is designed for learning the topology of the PFs with the solutions generated during a period of the search process as the training data. We use the individuals in the current population as well as those in previous generations to train the GNG to strike a balance between exploration and exploitation. Comparative studies conducted on popular benchmark problems and a real-world hybrid vehicle controller design problem with complex and irregular PFs show that the proposed method is very competitive.

  • Surrogate-Assisted Evolutionary Optimization of Large Problems
    High-Performance Simulation-Based Optimization, 2020
    Co-Authors: Tinkle Chugh, Chaoli Sun, Handing Wang, Yaochu Jin
    Abstract:

    This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is large, or both. These problems pose challenges to evolutionary algorithms themselves, constructing surrogates and surrogate management. To address these challenges, we proposed two algorithms, one called kriging-assisted Reference Vector guided evolutionary algorithm (K-RVEA) for many-objective optimization, and the other called cooperative swarm optimization algorithm (SA-COSO) for high-dimensional single-objective optimization. Empirical studies demonstrate that K-RVEA works well for many-objective problems having up to ten objectives, while SA-COSA outperforms the state-of-the-art algorithms on 200-dimensional single-objective test problems.

  • A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization
    IEEE Transactions on Evolutionary Computation, 2018
    Co-Authors: Tinkle Chugh, Yaochu Jin, Kaisa Miettinen, Jussi Hakanen, Karthik Sindhya
    Abstract:

    We propose a surrogate-assisted Reference Vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optimization that relies on a set of adaptive Reference Vectors for selection. The proposed surrogate-assisted EA (SAEA) uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the Reference Vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art SAEAs on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.

  • A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
    IEEE Transactions on Evolutionary Computation, 2016
    Co-Authors: Ran Cheng, Yaochu Jin, Markus Olhofer, Bernhard Sendhoff
    Abstract:

    In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user pReferences because it will be less likely to achieve a representative subset of the Pareto-optimal solutions using a limited population size as the number of objectives increases. This paper proposes a Reference Vector-guided EA for many-objective optimization. The Reference Vectors can be used not only to decompose the original multiobjective optimization problem into a number of single-objective subproblems, but also to elucidate user pReferences to target a preferred subset of the whole Pareto front (PF). In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the Reference Vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art EAs for many-objective optimization. In addition, we show that Reference Vectors are effective and cost-efficient for pReference articulation, which is particularly desirable for many-objective optimization. Furthermore, a Reference Vector regeneration strategy is proposed for handling irregular PFs. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.

  • SSCI - Connections of Reference Vectors and different types of pReference information in interactive multiobjective evolutionary algorithms
    2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016
    Co-Authors: Jussi Hakanen, Tinkle Chugh, Yaochu Jin, Karthik Sindhya, Kaisa Miettinen
    Abstract:

    We study how different types of pReference information coming from a human decision maker can be utilized in an interactive multiobjective evolutionary optimization algorithm (MOEA). The idea is to convert different types of pReference information into a unified format which can then be utilized in an interactive MOEA to guide the search towards the most preferred solution(s). The format chosen here is a set of Reference Vectors which is used within the interactive version of the Reference Vector guided evolutionary algorithm (RVEA). The proposed interactive RVEA is then applied to the multiple-disk clutch brake design problem with five objectives to demonstrate the potential of the idea in supporting decision making in optimization problems involving more than three objectives.

Stefan Posse - One of the best experts on this subject based on the ideXlab platform.

  • functional magnetic resonance imaging in real time fire sliding window correlation analysis and Reference Vector optimization
    Magnetic Resonance in Medicine, 2000
    Co-Authors: Daniel Gembris, John G. Taylor, Stefan Schor, W. Frings, Dieter Suter, Stefan Posse
    Abstract:

    New algorithms for correlation analysis are presented that allow the mapping of brain activity from functional MRI (fMRI) data in real time during the ongoing scan. They combine the computation of the correlation coefficients between measured fMRI time-series data and a Reference Vector with “detrending,” a technique for the suppression of non-stimulus-related signal components, and the “sliding-window technique.” Using this technique, which limits the correlation computation to the last N measurement time points, the sensitivity to changes in brain activity is maintained throughout the whole experiment. For increased sensitivity in activation detection a fast and robust optimization of the Reference Vector is proposed, which takes into account a realistic model of the hemodynamic response function to adapt the parameterized Reference Vector to the measured data. Based on the described correlation method, real-time fMRI experiments using visual stimulation paradigms have been performed successfully on a clinical MR scanner, which was linked to an external workstation for image analysis. Magn Reson Med 43:259 ‐268, 2000. © 2000 Wiley-Liss, Inc.

  • Functional magnetic resonance imaging in real time (FIRE): Sliding‐window correlation analysis and Reference‐Vector optimization
    Magnetic resonance in medicine, 2000
    Co-Authors: Daniel Gembris, John G. Taylor, Stefan Schor, W. Frings, Dieter Suter, Stefan Posse
    Abstract:

    New algorithms for correlation analysis are presented that allow the mapping of brain activity from functional MRI (fMRI) data in real time during the ongoing scan. They combine the computation of the correlation coefficients between measured fMRI time-series data and a Reference Vector with “detrending,” a technique for the suppression of non-stimulus-related signal components, and the “sliding-window technique.” Using this technique, which limits the correlation computation to the last N measurement time points, the sensitivity to changes in brain activity is maintained throughout the whole experiment. For increased sensitivity in activation detection a fast and robust optimization of the Reference Vector is proposed, which takes into account a realistic model of the hemodynamic response function to adapt the parameterized Reference Vector to the measured data. Based on the described correlation method, real-time fMRI experiments using visual stimulation paradigms have been performed successfully on a clinical MR scanner, which was linked to an external workstation for image analysis. Magn Reson Med 43:259 ‐268, 2000. © 2000 Wiley-Liss, Inc.

Daniel Gembris - One of the best experts on this subject based on the ideXlab platform.

  • functional magnetic resonance imaging in real time fire sliding window correlation analysis and Reference Vector optimization
    Magnetic Resonance in Medicine, 2000
    Co-Authors: Daniel Gembris, John G. Taylor, Stefan Schor, W. Frings, Dieter Suter, Stefan Posse
    Abstract:

    New algorithms for correlation analysis are presented that allow the mapping of brain activity from functional MRI (fMRI) data in real time during the ongoing scan. They combine the computation of the correlation coefficients between measured fMRI time-series data and a Reference Vector with “detrending,” a technique for the suppression of non-stimulus-related signal components, and the “sliding-window technique.” Using this technique, which limits the correlation computation to the last N measurement time points, the sensitivity to changes in brain activity is maintained throughout the whole experiment. For increased sensitivity in activation detection a fast and robust optimization of the Reference Vector is proposed, which takes into account a realistic model of the hemodynamic response function to adapt the parameterized Reference Vector to the measured data. Based on the described correlation method, real-time fMRI experiments using visual stimulation paradigms have been performed successfully on a clinical MR scanner, which was linked to an external workstation for image analysis. Magn Reson Med 43:259 ‐268, 2000. © 2000 Wiley-Liss, Inc.

  • Functional magnetic resonance imaging in real time (FIRE): Sliding‐window correlation analysis and Reference‐Vector optimization
    Magnetic resonance in medicine, 2000
    Co-Authors: Daniel Gembris, John G. Taylor, Stefan Schor, W. Frings, Dieter Suter, Stefan Posse
    Abstract:

    New algorithms for correlation analysis are presented that allow the mapping of brain activity from functional MRI (fMRI) data in real time during the ongoing scan. They combine the computation of the correlation coefficients between measured fMRI time-series data and a Reference Vector with “detrending,” a technique for the suppression of non-stimulus-related signal components, and the “sliding-window technique.” Using this technique, which limits the correlation computation to the last N measurement time points, the sensitivity to changes in brain activity is maintained throughout the whole experiment. For increased sensitivity in activation detection a fast and robust optimization of the Reference Vector is proposed, which takes into account a realistic model of the hemodynamic response function to adapt the parameterized Reference Vector to the measured data. Based on the described correlation method, real-time fMRI experiments using visual stimulation paradigms have been performed successfully on a clinical MR scanner, which was linked to an external workstation for image analysis. Magn Reson Med 43:259 ‐268, 2000. © 2000 Wiley-Liss, Inc.

Ran Cheng - One of the best experts on this subject based on the ideXlab platform.

  • A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
    IEEE Transactions on Evolutionary Computation, 2016
    Co-Authors: Ran Cheng, Yaochu Jin, Markus Olhofer, Bernhard Sendhoff
    Abstract:

    In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user pReferences because it will be less likely to achieve a representative subset of the Pareto-optimal solutions using a limited population size as the number of objectives increases. This paper proposes a Reference Vector-guided EA for many-objective optimization. The Reference Vectors can be used not only to decompose the original multiobjective optimization problem into a number of single-objective subproblems, but also to elucidate user pReferences to target a preferred subset of the whole Pareto front (PF). In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the Reference Vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art EAs for many-objective optimization. In addition, we show that Reference Vectors are effective and cost-efficient for pReference articulation, which is particularly desirable for many-objective optimization. Furthermore, a Reference Vector regeneration strategy is proposed for handling irregular PFs. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.

  • Reference Vector based a posteriori pReference articulation for evolutionary multiobjective optimization
    Congress on Evolutionary Computation, 2015
    Co-Authors: Ran Cheng, Markus Olhofer, Yaochu Jin
    Abstract:

    Multiobjective evolutionary algorithms (MOEAs) usually achieve a set of nondominated solutions as the approximation of the Pareto front. In order to utilize the solutions, a final decision making process is indispensable in most cases in which a small number of solutions have to be selected. In this process a decision maker selects the solutions according to his or her pReferences or based on the knowledge acquired by observing the approximated Pareto front. Due to the limited number of solutions an algorithm can obtain, in particular when the number of objectives is large, a decision maker may be interested in sampling additional solutions in some preferred regions. This paper proposes to use a Reference Vector based pReference articulation (RVPA) method to obtain such additional solutions in preferred regions. After describing the proposed method in detail, experiments are conducted on six benchmark MOPs to assess the performance of the proposed RVPA method. Our empirical results show that, by setting Reference Vectors in the objective space, the proposed RVPA is able to obtain corresponding solutions in the preferred regions at a much lower cost compared to e.g. a re-start strategy. In addition, by setting the Reference Vectors in a uniform way, the proposed RVPA method is also able to improve the general quality (convergence and distribution) of the solutions obtained by an MOEA.

  • adaptive Reference Vector generation for inverse model based evolutionary multiobjective optimization with degenerate and disconnected pareto fronts
    International Conference on Evolutionary Multi-criterion Optimization, 2015
    Co-Authors: Ran Cheng, Yaochu Jin, Kaname Narukawa
    Abstract:

    Inverse model based multiobjective evolutionary algorithm aims to sample candidate solutions directly in the objective space, which makes it easier to control the diversity of non-dominated solutions in multiobjective optimization. To facilitate the process of inverse modeling, the objective space is partitioned into several subregions by predefining a set of Reference Vectors. In the previous work, the Reference Vectors are uniformly distributed in the objective space. Uniformly distributed Reference Vectors, however, may not be efficient for problems that have nonuniform or disconnected Pareto fronts. To address this issue, an adaptive Reference Vector generation strategy is proposed in this work. The basic idea of the proposed strategy is to adaptively adjust the Reference Vectors according to the distribution of the candidate solutions in the objective space. The proposed strategy consists of two phases in the search procedure. In the first phase, the adaptive strategy promotes the population diversity for better exploration, while in the second phase, the strategy focused on convergence for better exploitation. To assess the performance of the proposed strategy, empirical simulations are carried out on two DTLZ benchmark problems, namely, DTLZ5 and DTLZ7, which have a degenerate and a disconnected Pareto front, respectively. Our results show that the proposed adaptive Reference Vector strategy is promising in tacking multiobjective optimization problems whose Pareto front is disconnected.

  • EMO (1) - Adaptive Reference Vector Generation for Inverse Model Based Evolutionary Multiobjective Optimization with Degenerate and Disconnected Pareto Fronts
    Lecture Notes in Computer Science, 2015
    Co-Authors: Ran Cheng, Yaochu Jin, Kaname Narukawa
    Abstract:

    Inverse model based multiobjective evolutionary algorithm aims to sample candidate solutions directly in the objective space, which makes it easier to control the diversity of non-dominated solutions in multiobjective optimization. To facilitate the process of inverse modeling, the objective space is partitioned into several subregions by predefining a set of Reference Vectors. In the previous work, the Reference Vectors are uniformly distributed in the objective space. Uniformly distributed Reference Vectors, however, may not be efficient for problems that have nonuniform or disconnected Pareto fronts. To address this issue, an adaptive Reference Vector generation strategy is proposed in this work. The basic idea of the proposed strategy is to adaptively adjust the Reference Vectors according to the distribution of the candidate solutions in the objective space. The proposed strategy consists of two phases in the search procedure. In the first phase, the adaptive strategy promotes the population diversity for better exploration, while in the second phase, the strategy focused on convergence for better exploitation. To assess the performance of the proposed strategy, empirical simulations are carried out on two DTLZ benchmark problems, namely, DTLZ5 and DTLZ7, which have a degenerate and a disconnected Pareto front, respectively. Our results show that the proposed adaptive Reference Vector strategy is promising in tacking multiobjective optimization problems whose Pareto front is disconnected.

  • CEC - Reference Vector based a posteriori pReference articulation for evolutionary multiobjective optimization
    2015 IEEE Congress on Evolutionary Computation (CEC), 2015
    Co-Authors: Ran Cheng, Markus Olhofer, Yaochu Jin
    Abstract:

    Multiobjective evolutionary algorithms (MOEAs) usually achieve a set of nondominated solutions as the approximation of the Pareto front. In order to utilize the solutions, a final decision making process is indispensable in most cases in which a small number of solutions have to be selected. In this process a decision maker selects the solutions according to his or her pReferences or based on the knowledge acquired by observing the approximated Pareto front. Due to the limited number of solutions an algorithm can obtain, in particular when the number of objectives is large, a decision maker may be interested in sampling additional solutions in some preferred regions. This paper proposes to use a Reference Vector based pReference articulation (RVPA) method to obtain such additional solutions in preferred regions. After describing the proposed method in detail, experiments are conducted on six benchmark MOPs to assess the performance of the proposed RVPA method. Our empirical results show that, by setting Reference Vectors in the objective space, the proposed RVPA is able to obtain corresponding solutions in the preferred regions at a much lower cost compared to e.g. a re-start strategy. In addition, by setting the Reference Vectors in a uniform way, the proposed RVPA method is also able to improve the general quality (convergence and distribution) of the solutions obtained by an MOEA.

Rajashekar P. Mandi - One of the best experts on this subject based on the ideXlab platform.