Article
Computer Science, Artificial Intelligence
Rajesh Ranjan, Jitender Kumar Chhabra
Summary: This study proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism. MO-CSACFS is implemented over several datasets to assess its performance, and it is compared with other similar algorithms. The results show that MO-CSACFS produces compact and robust clusters comparable to other works from the literature.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yesim A. Baysal, Seniha Ketenci, Ismail H. Altas, Temel Kayikcioglu
Summary: The study introduces a non-dominated sorting multi-objective symbiotic organism search algorithm for feature selection in brain-computer interface systems, which shows promising results in improving classification accuracy and reducing the number of features in two datasets. It verifies the superior performance of the proposed method compared to existing techniques, indicating its efficiency and practicality for BCI applications.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Guangdong Tian, Amir M. Fathollahi-Fard, Yaping Ren, Zhiwu Li, Xingyu Jiang
Summary: This study proposes a new multi-objective scheduling model and utilizes a modified discrete gravitational search algorithm to solve the complex scheduling problem in forest fire emergency rescue. Through simulation in Heilongjiang Province and comparison with other algorithms, the accuracy and efficiency of the proposed method are demonstrated.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
R. J. Kuo, Muhammad Rakhmat Setiawan, Thi Phuong Quyen Nguyen
Summary: This study introduces a novel data analytics-based sequential clustering and classification (SCC) approach, named deep MOSCA-SCC, which integrates multi-objective sine-cosine algorithm (MOSCA), deep clustering technique, and classification algorithms. The method shows better performance in terms of clustering sum of squared error and classification accuracy compared to other benchmark algorithms.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Dan Dai, Zhiwen Yu, Weijie Huang, Yang Hu, C. L. Philip Chen
Summary: This paper proposes a multi-objective filter refinement scheme (MOFRS) to improve the robustness and stability of clustering performances by utilizing multiple diverse solutions. MOFRS selects a proper method and reduces the number of initial partitions using a solution filter, splits instances into stable and unstable groups using stability indices, and quantifies the goodness of base clustering solutions using objective functions based on diversity and quality. An improvement-oriented multi-objective evolutionary algorithm is also designed to optimize these objective functions.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Songbai Liu, Junhao Zheng, Qiuzhen Lin, Kay Chen Tan
Summary: This paper proposes an MOEA using clustering with a flexible similarity metric to tackle multi and many-objective optimization problems with irregular Pareto fronts. By predicting the concavity or convexity of the problem and setting a flexible reference point, the algorithm can properly measure the similarity between solutions and classify them effectively in environmental selection, showing significant advantages in experimental results.
INFORMATION SCIENCES
(2021)
Article
Biology
Jayashree Piri, Puspanjali Mohapatra
Summary: A new MOQBHHO technique is proposed, utilizing KNN method to extract optimal feature subsets, with crowding distance used as a third criterion. Experimental findings show that this method outperforms other existing multi-objective techniques, effectively finding non-dominated feature subsets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
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, Artificial Intelligence
Yu Xue, Haokai Zhu, Jiayu Liang, Adam Slowik
Summary: Feature selection is a crucial pre-processing technique for classification, aiming to enhance classification accuracy by removing irrelevant or redundant features. This study introduces a multi-objective genetic algorithm with an adaptive operator selection mechanism, which effectively addresses high-dimensional feature selection problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Energy & Fuels
Xizheng Zhang, Zeyu Wang, Zhangyu Lu
Summary: The hybrid modified GSA-PSO scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles, which can significantly improve the safety and economy of the grid, reduce the total cost and load variance.
Article
Multidisciplinary Sciences
Wangang Cai, Yihao Zhang, Fuyou Huang, Chao Ma
Summary: This paper examines the electric vehicle routing problem with simultaneous pick-up and delivery and time window (EVRPSPDTW) in logistics distribution from the perspective of multi-objective distribution. It establishes a decision-making model based on distribution cost and power consumption function, and designs a multi-objective genetic algorithm (NSGA-II) optimization solution with various improvements. The proposed model and algorithm are proven to be effective through sensitivity analysis.
Article
Computer Science, Artificial Intelligence
Xianfeng Ou, Meng Wu, Bing Tu, Guoyun Zhang, Wujing Li
Summary: With the increasing spectral dimension of hyperspectral images, the correct choice of bands based on band correlation and information has become more significant and complicated. To address this, we propose a band selection method based on a multi-objective cuckoo search algorithm, which constructs a multi-objective unsupervised band selection model. The proposed method outperforms state-of-the-art algorithms in HSI classification experiments, demonstrating its effectiveness and robustness.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Adel Got, Abdelouahab Moussaoui, Djaafar Zouache
Summary: This paper introduces a novel hybrid filter-wrapper feature selection approach using whale optimization algorithm, which optimizes multiple objective functions simultaneously. Experimental results demonstrate the efficiency of the proposed algorithm on twelve benchmark datasets, showing its ability to obtain subsets with smaller number of features and excellent classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jie Qian, Ping Wang, Gonggui Chen
Summary: This paper proposes an improved multi-objective gravitational search algorithm (IMGSA), which demonstrates better population diversity and search capability for the multi-objective optimal active dispatching problem. By using an efficient power flow prediction model based on the radial basis function (RBF) network, the IMGSA-RBF method accurately determines multiple elite dispatching schemes for optimizing fuel cost, power loss, and exhaust emission. Experimental results show that the IMGSA-RBF method has significant advantages in terms of PF-uniformity, PF-diversity, and the quality of optimal dispatching schemes, providing a valuable technology for achieving desirable power grid operation with less carbon emission and better economy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Haibin Ouyang, Jianhong Chen, Steven Li, Jianhua Xiang, Zhi-Hui Zhan
Summary: This paper proposes an altruistic population algorithm (APA) based on altruism behavior in animal populations to solve complex and difficult multimodal multi-objective optimization problems (MMOPs). APA includes five major operations and introduces nurturing cost, altruism behavior, and neighboring selection based on distance in the objective space to accelerate convergence speed, improve efficiency, and eliminate redundant individuals. Experimental results demonstrate that APA outperforms other existing algorithms in solving various MMOPs.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)