Lam Thu Bui

Academy of Cryptography Techniques

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Hussein A. Abbass

UNSW Sydney

Van Truong Vu

Le Quy Don Technical University

Cao Truong Tran

Le Quy Don Technical University

Le-Minh Nguyen

Japan Advanced Institute of Science and Technology

Anh Viet Phan

Le Quy Don Technical University

Daryl Essam

University of Canberra

Michael Barlow

University of Canberra

Axel Bender

Defence Science and Technology Group

Zbigniew Michalewicz

Institute of Computer Science

Trung Thành Nguyễn

Hanoi Open University

Cooperative Institutions

Le Quy Don Technical University

UNSW Sydney

Institute of Electrical and Electronics Engineers

Hanoi University of Science and Technology

Australian Defence Force Academy

University of Canberra

Vietnam National University Ho Chi Minh City

Vietnam Academy of Science and Technology

Ho Chi Minh City University of Technology

UNSW Canberra

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A Pareto Corner Search Evolutionary Algorithm and Principal Component Analysis for Objective Dimensionality Reduction

Xuân Hùng Nguyễn Lam Thu Bui Cao Truong Tran

Many-objective optimisation problems (MaOPs) cause serious difficulties for existing multi-objective evolutionary algorithms (MOEAs). One common way to alleviate these difficulties is to use objective dimensionality reduction. Most existing objective reduction methods are time-consuming because they require MOEAs to run numerous generations. Pareto corner search evolutionary algorithm (PCSEA) was proposed in [18] to speed up objective reduction methods by only seeking corner solutions instead of whole solutions. However, the PCSEA-based objective reduction method in [18] needs to predefine a threshold to select objectives which strongly depends on problems and is not straightforward to obtain. This paper proposes a new objective dimensionality reduction method by integrating PCSEA and principal component analysis (PCA). Thanks to combining advantages of PCSEA and PCA, the proposed method not only can be efficient to eliminate redundant objectives, but also not require to define any parameter in advanced. The experimental results also show that the proposed method can perform objective reduction more successfully than the PCSEA-based objective reduction method. The results further strengthen the links between evolutionary computation and machine learning to address optimization problems.

10.1109/kse.2019.8919438

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Optimal Relay Placement for Maximizing Path Diversity in Multipath Overlay Networks

Vinh Bui Weiping Zhu Lam Thu Bui

Providing a high level of the path diversity is a substantial criterion in designing an overlay network. Given a underlying network infrastructure, the diversity of the overlay paths largely depends on how the overlay nodes, i.e. relays, are placed. In this paper, the problem of optimal relay placement is studied, and the goal is to maximize the diversity of the overlay paths while minimizing the number of relays. To tackle this multi-objective optimization problem, a novel genetic algorithm (GA) is applied hybridizing the standard GA and k-shortest path algorithms. By studying 6 real ISP topologies, we find that there exist non-unique solutions, which provide nearly the same level of the path diversity. In those solutions, only few locations are not replaceable allowing a great flexibility in the overlay network deployment. Perhaps surprisingly, these places do not include all those nodes with the highest number of links. The reason is that those nodes with the highest number of links are usually crossed by the default routing paths. Therefore, the overlay paths tend not to crossing them. We also notice that the number of relays required to maintain a high level of the path diversity is only 5% - 10% of the total number of network nodes depending on the topologies. We believe that our findings are useful for designing and deploying multi-path overlay networks.

Overlay network

10.1109/glocom.2008.ecp.301

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Forecasting of consumer price index using the ensemble learning model with multi-objective evolutionary algorithms: Preliminary results

Dinh Thi Thu Huong Van Truong Vu Lam Thu Bui

Time series forecasting is paid a considerable attention of the researchers. At present, in the field of machine learning, there are a lot of studies using an ensemble of artificial neural networks to construct the model for time series forecasting in general, and consumer price index (CPI) forecasting, in particular. However, determining the number of members of an ensemble is still debatable. This paper proposes the way of constructing a model for CPI forecasting and designing a multi-objective evolutionary algorithm in training neural networks ensembles in order to increase the diversity of the population. Two objectives of the training problem include: Mean Sum of Squared Errors and diversity. We experimented the model on three data sets and compared methods. The experimental results showed that the proposed model produced better in investigated cases.

Ensemble Learning

Ensemble forecasting

10.1109/atc.2015.7388346

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Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction

Lecture notes in computer science (2009)

Lam Thu Bui Michael Barlow

Memetic algorithm

10.1007/978-3-642-10427-5_26

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Fitness inheritance for noisy evolutionary multi-objective optimization

Lam Thu Bui Hussein A. Abbass Daryl Essam

This paper compares the performance of anti-noise methods, particularly probabilistic and re-sampling methods, using NSGA2. It then proposes a computationally less expensive approach to counteracting noise using re-sampling and fitness inheritance. Six problems with different difficulties are used to test the methods. The results indicate that the probabilistic approach has better convergence to the Pareto optimal front, but it looses diversity quickly. However, methods based on re-sampling are more robust against noise but they are computationally very expensive to use. The proposed fitness inheritance approach is very competitive to re-sampling methods with much lower computational cost.

Inheritance

Fitness approximation

10.1145/1068009.1068141

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A bi-population Genetic algorithm based on multi-objective optimization for a relocation scheme with target coverage constraints in mobile wireless sensor networks

Expert Systems with Applications (2023)

La Van Quan Nguyen Thi Hanh Huỳnh Thị Thanh Bình Vu Duc Toan Ngoc T. Dang

Maximization

10.1016/j.eswa.2022.119486

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Citations (13)