Article
Engineering, Chemical
Funan Peng, Li Lv, Weiru Chen, Jun Wang
Summary: In this paper, a projection-based evolutionary algorithm called MOEA/PII is introduced, which divides the objective space into projection plane and free dimension(s) using the idea of dimension reduction and decomposition. The balance between convergence and diversity is maintained using a Bi-Elite queue. MOEA/PII is an algorithm framework that can be combined with other decomposition-based or dominance-based algorithms, showing better performance.
Article
Automation & Control Systems
Yousef Abdi, Mohammad Asadpour, Yousef Seyfari
Summary: In this study, a hybrid micro multi-objective evolutionary algorithm called mu MOSM is proposed to effectively address diversity loss and accelerate the convergence rate in approximating Pareto front solutions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Babak Nouri-Moghaddam, Mehdi Ghazanfari, Mohammad Fathian
Summary: The study introduces a multi-objective feature selection algorithm based on the forest optimization algorithm, showing that it can reduce classification errors and feature numbers in most cases.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Ruochen Liu, Ping Yang, Haoyuan Lv, Weibin Li
Summary: This article proposes a multi-factorial evolutionary algorithm (MFEA) for solving the container placement problem in heterogeneous cluster environments. The MFEA algorithm, embedded with a local search strategy, significantly reduces optimization time and provides competitive solutions for container placement.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xiangjuan Yao, Qian Zhao, Dunwei Gong, Song Zhu
Summary: This article proposes a method to solve large-scale many-objective optimization problems (LSMaOPs) based on dimension reduction and a solving knowledge-guided evolutionary algorithm (KGEA). The method effectively reduces the dimension of the original problem by clustering and aggregating the objective functions, and then solves the reduced problem using the solving KGEA. Experimental results demonstrate the effectiveness of the proposed algorithm in tackling LSMaOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Theory & Methods
Shouyong Jiang, Juan Zou, Shengxiang Yang, Xin Yao
Summary: Evolutionary dynamic multi-objective optimisation (EDMO) is a rapidly growing area that uses evolutionary approaches to solve multi-objective optimisation problems with time-varying changes. After nearly two decades, significant advancements have been made in theoretic research and applications. This article provides a comprehensive survey and taxonomy of existing research on EDMO, as well as highlighting multiple research opportunities for further development.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
M. Sri Srinivasa Raju, Saykat Dutta, Rammohan Mallipeddi, Kedar Nath Das
Summary: The existence of constrained multi-objective optimization problems (CMOPs) has led researchers to develop constrained multi-objective evolutionary algorithms (CMOEAs). In order to handle CMOPs with discontinuous feasible regions or infeasible barriers, a novel Dual-Population and Multi-Stage based Constrained Multi-objective Evolutionary Algorithm (CMOEA-DPMS) is proposed, along with a new constraint handling technique (CHT) called decomposition based constraint non-dominating sorting (DCDSort) to maintain feasibility, convergence, and diversity.
INFORMATION SCIENCES
(2022)
Article
Multidisciplinary Sciences
Li Wang, Wei Wang
Summary: This study proposes a multi-objective optimization sparse decomposition algorithm for hyperspectral images. By optimizing the matching process of the orthogonal matching pursuit algorithm (OMP), the algorithm improves the performance and efficiency of sparse decomposition, and effectively solves the sparse decomposition problem of hyperspectral images.
Article
Computer Science, Information Systems
Haiping Ma, Haoyu Wei, Ye Tian, Ran Cheng, Xingyi Zhang
Summary: Constrained multi-objective optimization problems are challenging to handle due to the complexities of objectives and constraints. To address this issue, a multi-stage evolutionary algorithm is proposed in this paper, which gradually adds constraints and sorts their handling priority based on their impact on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in dealing with complex constraint problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Zhiwei Xu, Kai Zhang, Juanjuan He, Xiaoming Liu
Summary: In this research, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve multi-objective multi-task optimization problems. The algorithm improves convergence and diversity, and reduces negative information transfer through the information molecule concentration vector.
INFORMATION SCIENCES
(2022)
Article
Environmental Sciences
Xianyue Wang, Longxia Qian, Chengzu Bai, Jinde Cao
Summary: This paper introduces an unsupervised method for feature extraction in hyperspectral images, which improves the classification performance through multi-scale nonlinear edge analysis and feature fusion.
JOURNAL OF APPLIED REMOTE SENSING
(2023)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Statistics & Probability
Florian Gunsilius, Susanne Schennach
Summary: This article introduces a generalization of PCA to nonlinear settings, providing a new tool for dimension reduction and exploratory data analysis. The method has unique features and can be effectively applied in fields such as financial prediction.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2023)
Article
Computer Science, Artificial Intelligence
Chao Li, Handing Wang, Jun Zhang, Wen Yao, Tingsong Jiang
Summary: This article discusses the adversarial attack problem faced by deep neural networks and the limitations of existing solutions. An approximated gradient sign method using differential evolution is proposed to solve the black-box adversarial attack problem. By transforming the pixel-based decision space into a dimension-reduced decision space and introducing different neighbor selection and optimization search strategies, multiple variants of the proposed method are developed. Experimental results demonstrate the superior performance of the method in solving black-box adversarial attack problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zhenshou Song, Handing Wang, Hongbin Xu
Summary: This paper proposes a generic framework for expensive many-objective optimization using a Pareto-based bi-indicator infill sampling criterion. Empirical studies demonstrate the effectiveness and superiority of this framework and the incorporated algorithm in handling DTLZ problems with more than three objectives.
Article
Computer Science, Artificial Intelligence
Nan Zheng, Handing Wang, Bo Yuan
Summary: This paper proposes an adaptive model switch-based surrogate-assisted evolutionary algorithm, which utilizes radial basis function networks for denoising and adaptively selects sampling strategies based on maximizing the improvement in convergence, diversity, and approximation uncertainty to address noisy and expensive multi-objective optimization problems. The experimental results demonstrate that the proposed algorithm outperforms the five most advanced surrogate-assisted evolutionary algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Editorial Material
Computer Science, Artificial Intelligence
Handing Wang, Chaoli Sun, Jinliang Ding, Yew-soon Ong
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongcun Liu, Handing Wang
Summary: This study proposes a novel algorithm that combines global and local search strategies to address the challenge of multiple disconnected regions in the search space. The algorithm achieves competitive results with only hundreds of function evaluations and can handle mixed-variable optimization problems. The global module uses hybrid evolutionary operators and a Gower distance based surrogate model, while the local module performs competitive switching in different local regions and improves evaluation accuracy with local surrogate models. The algorithm is demonstrated to be effective through artificial benchmark tests and convolutional neural network hyperparameter optimization problems.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Chao Li, Wen Yao, Handing Wang, Tingsong Jiang
Summary: It has been found that deep neural networks are vulnerable to adversarial examples for several years. Existing transfer-based methods have weak transferability for black-box models and sparse attacks mainly focus on the number of attacked pixels without restricting the size of perturbations. To address these issues, this study proposes a transfer-based sparse attack method that improves transferability through adaptive momentum variance and refining perturbation mechanism, and uses a class activation map to explore the relationship between the number of perturbed pixels and attack performance.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Liang Fan, Handing Wang
Summary: To accelerate performance estimation in neural architecture search, a surrogate-assisted evolutionary algorithm with network embedding (SAENAS-NE) is proposed. Unsupervised learning generates meaningful representation for each architecture, making architecture with similar structures closer in the embedding space, benefiting surrogate model training. A new environmental selection based on reference population and an infill criterion for balancing convergence and model uncertainty are introduced. Experimental results demonstrate the superiority of SAENAS-NE over other state-of-the-art algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yapei Wu, Xingguang Peng, Handing Wang, Yaochu Jin, Demin Xu
Summary: Many real-world optimization tasks suffer from noise, but current research on noise-tolerant algorithms is limited to low-dimensional problems. This article proposes a landscape-aware grouping method for cooperative coevolutionary algorithms to solve high-dimensional problems under noisy environments. Experimental results show that the proposed method is able to effectively identify interactive decision variables in the presence of noise.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Junfeng Tang, Handing Wang, Lin Xiong
Summary: In preference-based multi-objective optimization, knee solutions are the implicit preferred promising solutions. However, finding knee solutions is difficult and computationally expensive. To address this issue, we propose a surrogate-assisted evolutionary multi-objective optimization algorithm that uses surrogate models to replace expensive evaluations. Experimental results show that our proposed algorithm outperforms state-of-the-art knee identification evolutionary algorithms on most test problems within a limited computational budget.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Nan Zheng, Handing Wang
Summary: For noisy bi-objective optimization problems, the algorithm is affected differently by noise in different stages of optimization. The proposed adaptive switch strategy enables the algorithm to adaptively switch among different noise treatments based on the noise impact. Additionally, data selection and model performance estimation methods are employed to enhance the denoising process, and reliable non-dominated solutions are selected as the final output. Experimental results demonstrate that the proposed algorithm is highly competitive for solving noisy bi-objective optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Chao Li, Wen Yao, Handing Wang, Tingsong Jiang, Xiaoya Zhang
Summary: Due to the importance of security, adversarial attacks in deep learning, especially the black-box adversarial attack, which mimics real-world scenarios, have gained popularity. Query-based methods are commonly used for black-box attacks but suffer from needing excessive queries. To overcome this, a Bayesian evolutionary optimization (BEO) based black-box attack method using differential evolution is proposed, employing Gaussian processes model and adaptive acquisition functions. Experimental results show that this method can effectively generate high-quality adversarial examples using only 200 queries.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Zhening Liu, Handing Wang, Yaochu Jin
Summary: Offline data-driven multiobjective optimization problems are common in practice. To address the issue of error accumulation when using surrogate models for optimization, a new surrogate-assisted indicator-based evolutionary algorithm is proposed. This algorithm can select the appropriate type of surrogate models based on the error, and it performs well in practical problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhenshou Song, Handing Wang, Yaochu Jin
Summary: Expensive constrained optimization problems can be solved by evolutionary algorithms in conjunction with computationally cheap surrogates. However, existing methods neglect the differences between different surrogate models, resulting in unsatisfactory performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiliang Zhao, Handing Wang, Wen Yao, Wei Peng, Zhiqiang Gong
Summary: Thermal layout optimization problems are common in integrated circuit design, where optimizing the positions of electronic components is essential for achieving low temperatures. Surrogate models are used to reduce evaluation costs, but they often have large prediction errors in discrete decision spaces such as thermal layout problems. A deep neural network is proposed in this work to better approximate the relation between layout schemes and temperature fields, leading to an online deep surrogate-assisted optimization algorithm that effectively manages parameters for improved performance within limited computational budgets.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xudong Feng, Zhening Liu, Feng Wu, Handing Wang
Summary: Traditional engine cycle innovation is limited by human experiences, imagination, and currently available engine component performance expectations. In this study, a mixed variable multi-objective evolutionary optimization method is proposed for automatic engine cycle design. Through experimental research, new engine cycle solutions have been discovered that surpass the performance of known turbojet and turbofan engines.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
