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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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multi-objective differential evolution for Dynamic Economic Emission Dispatch

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2014年第2期15卷 141-150页

作者： Basu, M. Jadavpur Univ Dept Power Engn Salt Lake CampusSec 3 Kolkata 700098 India

Dynamic economic emission dispatch is an important optimization task in fossil fuel-based power plant operation for allocating generation among the committed units with predicted load demands over a certain period of time such that fuel cost and emission level are optimized simultaneously. It is a highly constrained dynamic multi-objective optimization problem involving conflicting objectives. This paper proposes multi-objective differential evolution for dynamic economic emission dispatch problem. Numerical results for a sample test system have been presented to demonstrate the performance of the proposed algorithm. The results obtained from the proposed algorithm are compared with those obtained from nondominated sorting genetic algorithmII (NSGA-II).

关键词： dynamic economic dispatch dynamic economic emission dispatch multi-objective optimization multi-objective differential evolution

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multi-objective differential evolution application to irrigation planning

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ISH Journal of Hydraulic Engineering 2012年第1期18卷 54-64页

作者： Raju, K. Srinivasa Vasan, A. Gupta, Piyush Ganesan, Karthik Mathur, Hitesh Department of Civil Engineering Birla Institute of Technology and Science Hyderabad Pilani-Hyderabad Campus India Department of Civil Engineering Birla Institute of Technology and Science Pilani-Pilani Campus India

In the present study, applicability of multi-objective differential evolution (MODE) in irrigation planning perspective is demonstrated through a case study of Mahi Bajaj Sagar Project, Rajasthan, India. Three objectives, namely, net benefits, agricultural production and labour employment are analysed in the multi-objective environment. Non-dominated alternatives generated by MODE are reduced to a manageable subset with the help of K-means cluster analysis for effective decision making. Optimal number of groups is determined based on the cluster validation indices, namely, Davies–Bouldin and Dunn's. It is concluded that selection of suitable parameters is necessary for effective implementation of above methodologies in real-world planning situations. © 2012 Taylor & Francis Group, LLC.

关键词： Cluster analysis Cluster validation index India Irrigation planning multi-objective differential evolution

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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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Short term hydrothermal scheduling using multi-objective differential evolution with three chaotic sequences

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期47卷 85-99页

作者： Zhang, Huifeng Zhou, Jianzhong Zhang, Yongchuan Fang, Na Zhang, Rui Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

Short-term hydrothermal optimal scheduling with economic emission (SHOSEE) is a multi-objective and complex constrained optimization problem. In this paper, three chaotic sequences based multi-objective differential evolution (CS-MODE) is proposed to solve this SHOSEE problem, and it utilizes elitist archive mechanism to retain the non-dominated individuals, which improves the convergence ability in the differential evolution, and a heuristic two-step constraint-handling technique is utilized to handle those complex equality and inequality constraints in SHOSEE problem. Furthermore, in order to avoid premature convergence, the proposed CS-MODEs have integrated three chaotic mappings into differential evolution to implement population space search, it enlarges search space and enriches the diversity of individuals generated in evolution process. The compromise scheme is selected from the non-dominated set to represent the average efficiency level on the hydrothermal system, and the obtained simulation result also reveals the feasibility and effectiveness of proposed CS-MODEs in comparison to other methods established recently. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Hydrothermal optimal scheduling multi-objective differential evolution Premature convergence Chaotic mapping Constraint-handling Compromise scheme

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Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolution

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第1期242卷 10-20页

作者： Chen, Yingguo Mahalec, Vladimir Chen, Yingwu Liu, Xiaolu He, Renjie Sun, Kai Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China McMaster Univ Sch Computat Engn & Sci Hamilton ON L8S 4L7 Canada

A novel self-adaptive variable-size multi-objective differential evolution algorithm is presented to find the best reconfiguration of existing on-orbit satellites for some particular targets on the ground when an emergent requirement arises in a short period. The main contribution of this study is that three coverage metrics are designed to assess the performance of the reconfiguration. Proposed algorithm utilizes the idea of fixed-length chromosome encoding scheme combined with expression vector and the modified initialization, mutation, crossover and selection operators to search for optimal reconfiguration structure. multi-subpopulation diversity initialization is adopted first, then the mutation based on estimation of distribution algorithm and adaptive crossover operators are defined to manipulate variable-length chromosomes, and finally a new selection mechanism is employed to generate well-distributed individuals for the next generation. The proposed algorithm is applied to three characteristically different case studies, with the objective to improve the performance with respect to specified targets by minimizing fuel consumption and maneuver time. The results show that the algorithm can effectively find the approximate Pareto solutions under different topological structures. A comparative analysis demonstrates that the proposed algorithm outperforms two other related multi-objective evolutionary optimization algorithms in terms of quality, convergence and diversity metrics. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Satellite orbit reconfiguration Variable-size optimization multi-objective differential evolution evolutionary computations Estimation of Distribution Algorithm

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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 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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Hybrid multi-objective differential evolution (H-MODE) for optimisation of polyethylene terephthalate (PET) reactor

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2010年第3-4期2卷 213-221页

作者： Gujarathi, Ashish M. Babu, B. V. BITS Dept Chem Engn Pilani 333031 Rajasthan India

multi-objective evolutionary algorithms (MOEAs) are used to solve the optimisation problems with more than one objective to be optimised simultaneously having conflict among each other. Due to the limitations of traditional deterministic algorithms to handle complex and nonlinear search space, several EAs are developed in the recent past. The multi-objective differential evolution (MODE) algorithm is already tested and found to be a reliable algorithm due to their ability to handle non-linear problems efficiently. Though MODE is accurate in terms of converging to the global Pareto front, traditional method has their advantage in terms of speed. We combined these two algorithms and developed hybrid strategy of MODE thus, achieving both accuracy and speed. Hybrid MODE (H-MODE) algorithm is applied on multi-objective optimisation of industrial wiped film polyethylene terephthalate reactor. The results of the present study are compared with those obtained using MODE algorithm. Smooth and well diverse Pareto optimal front is observed with a much faster speed using H-MODE.

关键词： multi-objective optimisation MOO multi-objective differential evolution MODE modelling and simulation polyethylene terephthalate reactor evolutionary algorithms EAs hybrid algorithms H-MODE Pareto Front bio-inspired computation

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Assembly line balancing by a new multi-objective differential evolution algorithm based on TOPSIS

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2011年第10期49卷 2833-2855页

作者： Nourmohammadi, A. Zandieh, M. Shahid Beheshti Univ Dept Ind Management Management Accounting Fac GC Tehran Iran Qazvin Islamic Azad Univ Fac Ind & Mech Engn Qazvin Iran

In this paper, we propose a multi-objective differential evolution algorithm (MODEA) to solve the multi-objective simple assembly line balancing problem type-2 (SALBP-2). This problem arises when in an existing assembly line, changes in the production process or demand structure take place and the organisation wants to produce the optimum number of items using a fixed number of workstations, which is associated with optimally assigning the tasks to an ordered sequence of stations such that the precedence relations are not violated and some measures of performance are optimised. The two considered objectives are: minimising the cycle time and the smoothness index of the assembly line. To that purpose, we develop a MODEA which unlike the existing algorithms deals with the considered objectives separately in selecting the next population members by proposing a new acceptance scheme based on the Pareto dominance concept and a new evaluation scheme based on TOPSIS. Also, by using the Taguchi method, we tune the effective factors of the developed algorithm. Then its efficiency is tested over available assembly line balancing benchmarks and compared to a new algorithm provided recently in the bi-objective SALBP-2 literature. Computational experiments indicate that the developed algorithm outperforms the existing meta-heuristic over a large group of benchmarks.

关键词： assembly line balancing multi-objective differential evolution TOPSIS Taguchi design

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