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A high-dimensional feature selection algorithm via fast dimensionality reduction and multi-objective differential evolution

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SWARM AND evolutionARY COMPUTATION 2025年 94卷

作者： Yue, Xuezhi Liao, Yihang Peng, Hu Kang, Lanlan Zeng, Yuan Jiangxi Univ Sci & Technol Coll Sci Ganzhou 341000 Jiangxi Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Jiangxi Peoples R China Gannan Univ Sci & Technol Coll Informat Engn Ganzhou 341000 Jiangxi Peoples R China Guangdong Commun Polytech Inst Mech & Elect Engn Qingyuan 511500 Guangdong Peoples R China

The multi-objective feature selection problem typically involves two key objectives: minimizing the number of selected features and maximizing classification performance. However, most multi-objective evolutionary algorithms (MOEAs) face challenges in high-dimensional datasets, including low search efficiency and potential loss of search space. To address these challenges, this paper proposes a hybrid algorithm based on fast dimensionality reduction and multi-objective differential evolution with redundant and preference processing (termed DR-RPMODE). In DR-RPMODE, the DR phase uses the freezing and activation operators to remove many irrelevant and redundant features in the high-dimensional datasets, thereby achieving fast dimensionality reduction. Subsequently, the RPMODE algorithm continues the search on the reduced datasets, improving the traditional differential evolutionary framework from two aspects: duplicated and redundant solutions are filtered by redundant handling, and a preference handling method that pays more attention to classification performance is designed for different preference objectives of decision-makers. In the experiment, DR-RPMODE is compared with seven feature selection algorithms on 16 classification datasets. The results indicate that DRRPMODE outperforms the comparison algorithms on most datasets, demonstrating that it not only achieves outstanding optimization performance but also obtains good classification and scalability results.

关键词： Feature selection High-dimensional datasets Fast dimensionality reduction multi-objective differential evolution

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AMoDeBic: An adaptive multi-objective differential evolution biclustering algorithm of microarray data using a biclustering binary mutation operator

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期238卷

作者： Charfaoui, Younes Houari, Amina Boufera, Fatma Univ Mustapha STAMBOULI Mascara Mascara Algeria

In bioinformatics, biclustering is a crucial optimization task that can reveal hidden patterns and identify groups of genes that behave similarly under certain conditions. This study aims to efficiently identify high-quality and cohesive biclusters that share common characteristics across two data dimensions. To achieve this, we propose the first biclustering approach that utilizes multi-objective differential evolution (DE), which is a novel technique for gene group discovery. Additionally, we introduce the Biclustering Binary differential evolution (BBDE), a new mutation operator that combines node addition and deletion, guided by an adaptive factor F. We thoroughly tested our method's effectiveness taking into account biological relevance, noise, overlap resistance, and statistics. We compared our results to state of the art algorithms using both synthetic and real datasets like Yeast Cell Cycle, Saccharomyces cerevisiae, and Human B Cell. Our algorithms outperformed the comparisons and effectively identified significant biclusters.

关键词： differential evolution algorithms multi-objective differential evolution Biclustering evolutionary algorithms Micro-array data Knowledge discovery

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Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm

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SOFT COMPUTING 2021年第6期25卷 4489-4502页

作者： Ren, Weijie Wang, Yiwen Han, Min Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China

For echo state networks, it is difficult to select suitable reservoir parameters for different applications. In this paper, we put forward a divided adaptive multi-objective differential evolution (DAMODE) algorithm to optimize the reservoir parameters of echo state network. To improve the performance of multi-objective differential evolution algorithm, the entire population is divided into several subpopulations, and each subpopulation is divided into two subsets to compromise convergence and diversity, which are updated according to certain rules. Besides, the scale factor and crossover rate of differential evolutionary algorithm are adaptively adjusted. Experiments were conducted on the Lorenz time series, hourly temperature time series and PM2.5 time series in Beijing. Experiment results show that the proposed model can improve prediction accuracy and has good generalization ability and practicability.

关键词： Time series Prediction multi-objective differential evolution Echo state network

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Reinforcement-Learning-Based multi-objective differential evolution Algorithm for Large-Scale Combined Heat and Power Economic Emission Dispatch

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ENERGIES 2023年第9期16卷 3753页

作者： Chen, Xu Fang, Shuai Li, Kangji Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

As social and environmental issues become increasingly serious, both fuel costs and environmental impacts should be considered in the cogeneration process. In recent years, combined heat and power economic emission dispatch (CHPEED) has become a crucial optimization problem in power system management. In this paper, a novel reinforcement-learning-based multi-objective differential evolution (RLMODE) algorithm is suggested to deal with the CHPEED problem considering large-scale systems. In RLMODE, a Q-learning-based technique is adopted to automatically adjust the control parameters of the multi-objective algorithm. Specifically, the Pareto domination relationship between the offspring solution and the parent solution is used to determine the action reward, and the most-suitable algorithm parameter values for the environment model are adjusted through the Q-learning process. The proposed RLMODE was applied to solve four CHPEED problems: 5, 7, 100, and 140 generating units. The simulation results showed that, compared with four well-established multi-objective algorithms, the RLMODE algorithm achieved the smallest cost and smallest emission values for all four CHPEED problems. In addition, the RLMODE algorithm acquired better Pareto-optimal frontiers in terms of convergence and diversity. The superiority of RLMODE was particularly significant for two large-scale CHPEED problems.

关键词： economic emission dispatch combined heat and power multi-objective differential evolution reinforcement learning large-scale system

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A new multi-objective differential evolution approach for simultaneous clustering and feature selection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 87卷 103307-000页

作者： Hancer, Emrah Mehmet Akif Ersoy Univ Dept Comp Technol & Informat Syst TR-15039 Burdur Turkey

Today's real-world data mostly involves incomplete, inconsistent, and/or irrelevant information that causes many drawbacks to transform it into an understandable format. In order to deal with such issues, data preprocessing is a proven discipline in data mining. One of the typical tasks in data preprocessing, feature selection aims to reduce the dimensionality in the data and thereby contributes to further processing. Feature selection is widely used to enhance the performance of a supervised learning algorithm (e.g., classification) but is rarely used in unsupervised tasks (e.g., clustering). This paper introduces a new multi-objective differential evolution approach in order to find relatively homogeneous clusters without the prior knowledge of cluster number using a smaller number of features from all available features in the data. To analyze the goodness of the introduced approach, several experiments are conducted on a various number of real-world and synthetic benchmarks using a variety of clustering approaches. From the analyzes through several different criteria, it is suggested that our method can significantly improve the clustering performance while reducing the dimensionality at the same time.

关键词： Clustering Feature selection Automatic clustering multi-objective differential evolution

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A multi-objective discrete differential evolution algorithm for energy-efficient two-stage flow shop scheduling under time-of-use electricity tariffs

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APPLIED SOFT COMPUTING 2023年 133卷

作者： Xue, Ling Wang, Xiuli Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China

In response to the appeal for environmental protection and the efficient utilization of energy, local governments in China actively promote time-of-use (TOU) electricity tariffs for manufacturing enterprises. Motivated by a real-life industrial scenario of large special-purpose pressure vessel production, this paper addresses an energy-efficient two-stage flow shop scheduling problem under TOU tariffs to minimize the total electricity cost and the mean tardiness. Since the problem is computationally intractable, we focus on developing a multi-objective discrete differential evolution (MDDE) algorithm. Specifically, based on the optimal properties of the problem, we tailor an encoding scheme that consists of two job sequences and an idle time vector. The novel mutation and crossover operators are designed to generate the trail individuals, and the hypervolume contribution indicator is incorporated into the bi-criteria selection operator to measure the quality of the solution. Furthermore, two neighborhood structures are designed to iteratively improve the non-dominated solutions in the external archive set. We evaluate the performance of the MDDE algorithm via extensive computational experiments. The experimental results indicate that the MDDE algorithm can obtain the good approximate Pareto front, and it outperforms the well-known NSGA-II, SPEA2 and MOEA/D algorithms in solution quality.& COPY;2022 Elsevier B.V. All rights reserved.

关键词： Two-stage flow shop scheduling Time-of-use tariffs Electricity cost Mean tardiness multi-objective differential evolution algorithm

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A hybrid model of multi-objective differential evolution algorithm and various decision-making methods to optimize the batch ABE fermentation process

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SCIENTIA IRANICA 2019年第6期26卷 3401-3414页

作者： Khademi, M. H. Lak, S. Zandi Univ Isfahan Fac Engn Dept Chem Engn POB 81746-73441 Esfahan Iran Islamic Azad Univ Dept Chem Engn Shiraz Branch POB 71987-74731 Shiraz Iran

In recent years, biofuels have attracted considerable attention as a renewable and clean source of energy and have been playing the role of suitable alternatives to fossil fuels. One of the most attractive types of biofuels is Acetone-Butanol-Ethanol (ABE), which is produced in a batch fermentation process by the anaerobic bacterium Clostridium acetobutylicum and sugar-based substrate as feedstock. In this paper, the optimization of this process was carried out according to a bi-objective function. A hybrid model of multi-objective differential evolution (MODE) algorithm and distinguished decision-making methods, namely linear programming technique for multidimensional analysis of preference (LINMAP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Shannon's entropy, were applied to find the final optimal operating point. The initial concentration of substrate and the final operating time of the process were selected as decision variables to maximize the two main objectives in terms of solvent yield and productivity. A Pareto optimal set presents a wide range of optimal operating points, and a proper operating condition can be selected based on the necessities of the applicant. The best optimal point obtained by TOPSIS, according to the lowest value of deviation index, was also compared with the results of the economy-based optimization. (C) 2019 Sharif University of Technology. All rights reserved.

关键词： ABE fermentation Clostridium acetobutylicum Optimization multi-objective differential evolution Decision-making method

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Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution

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APPLIED SOFT COMPUTING 2017年 61卷 58-71页

作者： Feng, Zhong-kai Niu, Wen-jing Zhou, Jian-zhong Cheng, Chun-tian Zhang, Yong-chuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Hubei Peoples R China Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China

With the growing concerns on energy and environment, the short-term hydrothermal scheduling (SHTS) which minimizes the fuel cost and pollutant emission simultaneously is playing an increasing important role in the modern electric power system. Due to the complicated operation constraints and objectives, SHTS is classified as a multi-objective optimization problem. Thus, to efficiently resolve this problem, this paper develops a novel parallel multi-objective differential evolution (PMODE) combining the merits of parallel technology and multi-objective differential evolution. In PMODE, the population with larger size is first divided into several smaller subtasks to be concurrently executed in different computing units, and then the main thread collects the results of each subpopulation to form the final Pareto solutions set for the SHTS problem. During the evolutionary process of each subpopulation, the mutation crossover and selection operators are modified to enhance the performance of population. Besides, an external archive set is used to conserve the Pareto solutions and provide multiple evolutionary directions for individuals, while the constraint handling method is introduced to address the complicated operational constraints. The results from a mature hydrothermal system indicate that when compared with several existing methods, PMODE can obtain satisfactory solutions in both fuel cost and environmental pollutant, which is an effective tool to generate trade-off schemes for the hydrothermal scheduling problem. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Hydrothermal scheduling multi-objective differential evolution Parallel computing Fork/Join framework Constraint handling method

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A grid-based adaptive multi-objective differential evolution algorithm

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INFORMATION SCIENCES 2016年 367卷 890-908页

作者： Cheng, Jixiang Yen, Gary G. Zhang, Gexiang Southwest Petr Univ Sch Elect Engn & Informat Chengdu 610500 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74074 USA Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

differential evolution is an excellent optimizer for single objective optimization problems. To extend its use for multi-objective optimization problems with promising performance, this paper proposes a grid-based adaptive multi-objective differential evolution algorithm. The main feature of the proposed algorithm is its dynamical adjustment of convergence and diversity by exploiting the feedback information during the evolutionary process. In the algorithm, the objective space is divided into grids according to the nondominated solutions in the population. Based on the grid, three indexes including grid fitness, grid density, and grid-objective-wise standard deviation are defined to measure individual rank, individual density, and population search status quo, respectively. Afterwards, three main components of the algorithm, i.e., parents selection, parameter control, and population update, are implemented based on grid index values. To validate algorithm performance, comprehensive experiments are carried out on thirty-one benchmark problems. The results show that the proposed algorithm outperforms nine state-of-the-art competitors in terms of three performance metrics. Also, the effectiveness of three components and the sensitivity of two design parameters in the algorithm are empirically quantified. (C) 2016 Elsevier Inc. All rights reserved.

关键词： multi-objective optimization multi-objective differential evolution Grid Parameter adaptation

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Fuzzy control optimized by a multi-objective differential evolution algorithm for vibration suppression of smart structures

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COMPUTERS & STRUCTURES 2015年第Jan.期147卷 126-137页

作者： Marinaki, Magdalene Marinakis, Yannis Stavroulakis, Georgios E. Tech Univ Crete Sch Prod Engn & Management Khania Greece

Smart structures include elements of active, passive or hybrid control. The fuzzy control is considered which is a suitable tool for the systematic development of active control strategies. A new multi-objective differential evolution algorithm for the calculation of the free parameters in active control systems is proposed and tested. In particular, the usage of MODE with a combination of continuous and discrete variables for the optimal design of the controller is proposed. Numerical applications on smart piezoelastic beams are presented. The results obtained are compared with the ones obtained with the fuzzy controller optimized by a MOPSO algorithm. (C) 2014 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.

关键词： multi-objective differential evolution multi-objective particle swarm optimization Active control of structures Fuzzy control Smart structures

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