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grey prediction evolution algorithm Based on Accelerated Even grey Model

IEEE ACCESS 2020年 8卷 107941-107957页

作者： Gao, Cong Hu, Zhongbo Xiong, Zenggang Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou 434023 Peoples R China Hubei Engn Univ Sch Comp & Informat Sci Xiaogan 432000 Peoples R China

The grey prediction evolution algorithm based on the even grey model (GPEAe) is a pioneer of prediction-based evolutionary algorithms. Its offsprings are generated by a first-order inverse accumulating generation operation (1-IAGO) depending on every prediction of the even grey model. For the fact that the original values are already known for the first few predicted values in 1-IAGO, this paper firstly develops an accelerated 1-IAGO (1-AIAGO) which replaces a particular prediction with the corresponding original value. An accelerated even grey model (AEGM(1,1)) based on the 1-AIAGO is then proposed. Finally, this paper proposes a new grey prediction evolution algorithm (GPEAae) which uses the AEGM(1,1) as the reproduction operator of evolutionary algorithm to forecast the offspring. The performance of GPEAae is verified on the CEC2014 benchmark functions and the set containing nine engineering constrained design problems. The experimental results show that the GPEAae has superiority and highly competitive effectiveness when compared with the GPEAe and other state-of-the-art algorithms. The motivation of the GPEAae is using the iterative frame of evolutionary algorithms to superimpose the weak effect generated by the proposed 1-AIAGO which replaces the approximate (predicted) value with the corresponding accurate (original) value. Matlab_Codes of this article can be found in https://***/Zhongbo-Hu/prediction-evolutionary-algorithm-HOMEPAGE.

关键词： Predictive models prediction algorithms Acceleration prediction theory Data models Approximation algorithms Optimization Accelerated even grey model engineering constrained design problem grey prediction evolution algorithm

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An Improved grey prediction evolution algorithm Based on Topological Opposition-Based Learning

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IEEE ACCESS 2020年 8卷 30745-30762页

作者： Dai, Canyun Hu, Zhongbo Li, Zheng Xiong, Zenggang Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou 434020 Peoples R China Hubei Engn Univ Sch Comp & Informat Sci Xiaogan 432000 Peoples R China

The grey prediction evolution algorithm based on the even grey model (GPEAe) proposed by *** et al. in 2019 is a competitively stochastic real-parameter optimization algorithm with characters of simple code, less parameters and strong exploration capability. To improve the algorithmic overall performance, a topological opposition-based learning strategy (TOBL) is first developed to enhance its exploitation capability in this paper. The TOBL determines offsprings by calculating the Manhattan distances between the current best individual and all the vertices of the hypercube inspired by the opposition-based learning strategy. An improved grey prediction evolutionary algorithm based on the TOBL (TOGPEAe) is then proposed. The performance of the TOGPEAe is tested on CEC2005, CEC2014 benchmark functions and a test suite composed of six engineering design problems. The experimental results of the TOGPEAe are very competitive compared with those of the original GPEAe and other state-of-the-art algorithms.

关键词： Engineering design problems grey prediction evolution algorithm topological opposition-based learning

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Hybridizing genetic algorithm with grey prediction evolution algorithm for solving unit commitment problem

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APPLIED INTELLIGENCE 2023年第17期53卷 19922-19939页

作者： Tong, Wangyu Liu, Di Hu, Zhongbo Su, Qinghua Hubei Univ Technol Sch Comp Sci Wuhan Hubei Peoples R China Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

The unit commitment problem (UCP), which includes the unit schedule and power dispatch, is a nonlinear high-dimensional and highly constrained mixed-integer combinatorial optimization problem. One challenge herein is to obtain high-quality solutions considering various constraints. Developing a competitive hybrid method is a mainstream study goal in this field, which has focused on the unit schedule optimization but less on power dispatch. Inspired by the advantage of genetic algorithms (GAs) in solving combinational optimization problems and the characteristic of grey prediction evolution algorithm (GPE) with strong exploration ability, this paper proposes a novel hybrid GA and GPE method, termed hGAGPE, to solve the UCP. In hGAGPE, GPE, as a novel real parameter stochastic search algorithm based on the grey prediction theory for data mining, is first employed to solve the power dispatch of the UCP. Meanwhile, the unit schedule is performed by the popular GA. Additionally, some heuristic repair mechanisms based on the priority list and an elite selection mechanism are incorporated to enhance the performance of hGAGPE. The proposed hGAGPE is evaluated on six test systems with generating units in the range of 10 to 100 during a 24-h scheduling period. The numerical results demonstrate the feasibility and effectiveness of hGAGPE in comparison with other existing approaches.

关键词： Genetic algorithm grey prediction evolution algorithm Hybrid method Power dispatch Unit commitment

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A novel grey prediction evolution algorithm for multimodal multiobjective optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 100卷 104173-104173页

作者： Zhou, Ting Hu, Zhongbo Zhou, Quan Yuan, Shixiong Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

ABSTR A C T In practical applications, solving multimodal multiobjective optimization problems (MMOPs), which have multiple Pareto optimal sets (PSs) in the decision space mapping to the same Pareto front (PF) in the objective space, has great significance for decision makers. The issue of how to maintain diversity in both decision space and objective space remains a key problem for existing multimodal multiobjective evolutionary algorithms. To address this issue, a novel grey prediction evolution algorithm for multimodal multiobjective optimization, termed MMGPE, is proposed in this paper. This is the first time that the grey prediction evolution algorithm (GPE), which is a recently proposed competitive optimization algorithm with strong exploration capability, is improved for MMOPs. These improvements are conducted in the following four aspects: (1) an initialization operator based on particle swarm optimization, (2) an adaptive parameter setting strategy depending on the domain of decision variables of MMOPs, (3) an accelerating convergence mechanism inspired by the niche principle, and (4) an environmental selection operator based on a nondominated sorting mechanism and a special crowding distance approach. The performance of MMGPE is compared with two state-of-the-art multiobjective evolutionary algorithms and four multimodal multiobjective evolutionary algorithms on 11 multimodal multiobjective test functions. MMGPE is also applied to solve a practical problem. The results show the MMGPE?s effectiveness and superiority in achieving the goal of finding multiple PSs while obtaining a well-distributed PF.

关键词： grey prediction evolution algorithm Multimodal multiobjective evolutionary algorithms Multimodal multiobjective optimization problems

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A simplified non-equidistant grey prediction evolution algorithm for global optimization

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APPLIED SOFT COMPUTING 2022年 125卷

作者： Xiang, XiYang Su, QingHua Huang, Gang Hu, ZhongBo Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China China Univ Geosci Sch Math & Phys Wuhan Hubei Peoples R China

grey prediction evolution algorithm, which is receiving more and more attention, considers the population sequence of evolutionary algorithms as an equidistant time series. In fact, the average fitness value of each generation population decreases at a variable speed during almost any evolution process. Therefore, the population sequence with the property of variable speed evolution should be modeled more properly as a non-equidistant time series. Along this way, this paper proposes a novel evolutionary algorithm based on a non-equidistant grey model, called simplified non-equidistant grey prediction evolution algorithm. The proposed algorithm is identified by its reproduction operator which is developed by the following three steps. Firstly, a non-equidistant grey model based on the average fitness value of each generation population is modeled. Secondly, the interval in the fitting stage of the non-equidistant grey model is simplified to an approximately equidistant time interval. Finally, a simplified reproduction operator which employs a parameter to preserve the non-equidistant nature in the prediction stage of the non-equidistant grey model is developed. The comprehensive performance of the proposed algorithm is evaluated on CEC2014 and CEC2019, as well as six engineering constrained design problems. Experimental results show that the proposed algorithm ranks first in CEC2014 and CECE2019 benchmark functions. The proposed algorithm can achieve a better solution with fewer computational overhead than some state-of-the-art algorithms on six engineering constrained design problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： grey prediction evolution algorithm Non-equidistant grey model Engineering constrained design problems Meta-heuristic algorithm

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Non-equidistant grey prediction evolution algorithm: A mathematical model-based meta-heuristic technique

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SWARM AND evolutionARY COMPUTATION 2023年 78卷

作者： Xiang, Xiyang Su, Qinghua Hu, Zhongbo Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

A grey predictive evolutionary algorithm, which is attracting more and more attention, regards the population series of evolutionary algorithms as an equidistant time series. The population evolution is essentially regarded as a process in which the function values have variable-speed decrease with the increase of the number of iterations (for minimization problems). Therefore, a fitness-driven evolutionary population sequence with the property of variable-speed evolution should be more appropriately modelled as a non-equidistant time series. A novel meta-heuristic optimization algorithm, non-equidistant grey prediction evolution algorithm is proposed in this paper. The proposed algorithm is identified by its reproduction operator which is developed by the following two steps. Firstly, a non-equidistant grey model (NeGM (1,1)) based on the average fitness value of each generation population to preserve the non-equidistant nature is modelled. Secondly, the interval in the fitting stage of the NeGM (1,1) is defined as an increasing time interval. The performance of the proposed algorithm is evaluated on CEC2019 and CEC2020 benchmark functions. Experimental results show that the proposed algorithm is superior to other more complex and notable approaches, in terms of solving accuracy as well as the rate of *** Matlab code of this paper is availabled on https://***/Zhongbo-Hu/prediction-evolutionary-algorithm-HOMEPAGE.

关键词： grey prediction evolution algorithm Non-equidistant grey model Benchmark functions Meta-heuristic algorithm

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Automated test case generation for path coverage by using grey prediction evolution algorithm with improved scatter search strategy

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 106卷 104454-104454页

作者： Cai, Gaocheng Su, Qinghua Hu, Zhongbo Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China

Automated test case generation for path coverage (ATCG-PC), as an important task in software testing, aims to achieve the highest path coverage of a tested program by using as little computational overhead as possible. In ATCG-PC, "similar paths are usually executed by similar test cases"is a problem-specific knowledge which was touched by a handful of researchers but still underutilized. Inspired by the problem-specific knowledge, this paper designs a local search strategy by improving a scatter search strategy, and then proposes a grey prediction evolution algorithm with the improved scatter search strategy for ATCG-PC. Here, the improved scatter search strategy could obtain two feasible test cases by exploiting a dimension of a test case covering a certain path. The proposed algorithm is constructed by importing the improved scatter search strategy to the end of the reproduction operation of the grey prediction evolution algorithm holding strong exploration ability. grey prediction evolution algorithm is first applied to solve ATCG-PC. The performance of the proposed algorithm is evaluated on six fog computing benchmark programs and six natural language processing benchmark programs. The experimental results demonstrate that the proposed algorithm can achieve the highest path coverage with the fewer test cases and running time than some state-of-the-art algorithms.

关键词： Automated test case generation grey prediction evolution algorithm Improved scatter search strategy Path coverage

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Four adaptive grey prediction evolution algorithms with different types of parameters setting techniques

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SOFT COMPUTING 2022年第18期26卷 9247-9271页

作者： Gao, Cong Hu, Zhongbo Miao, Yongfei Zhang, Xiaowei Su, Qinghua Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Air Force Commun NCO Acad Dept Ground Air Nav Dalian Liaoning Peoples R China Univ Elect Sci & Technol China Sch Math Sci Chengdu Peoples R China

Generally, the performance of evolutionary algorithms (EAs) greatly depends on the setting of their control parameters. Many adaptive setting technologies have been developed to solve the time-consuming task of parameter settings. However, the adaptive technologies for the grey prediction evolution algorithm (GPE), which was proposed by Hu et al. in 2020 with two control parameters, have not been investigated. This paper proposes four adaptive GPE algorithms according to three typical adaptive technologies, i.e. probability distributions, statistics and mathematical models in the field of EAs. Four proposed adaptive GPE are separately based on the uniform distribution (aGPEu), normal distribution (aGPEn), average fitness (aGPEa) and chaotic sequences (aGPEc). The performance of the four proposed adaptive GPE algorithms is evaluated on CEC2014, CEC2017 benchmark functions and a suite of eight engineering constrained design problems. The experimental results show the superiority and competitiveness of all the four proposed algorithms.

关键词： Adaptive control parameter settings grey prediction evolution algorithm Probability distributions Statistics

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Underwater image enhancement method via extreme enhancement and ultimate weakening

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2024年 105卷

作者： Zhou, Yang Su, Qinghua Hu, Zhongbo Jiang, Shaojie Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Yangtze Univ Jingzhou Hosp Jingzhou Hubei Peoples R China

The existing histogram-based methods for underwater image enhancement are prone to over-enhancement, which will affect the analysis of enhanced images. However, an idea that achieves contrast balance by enhancing and weakening the contrast of an image can address the problem. Therefore, an underwater image enhancement method based on extreme enhancement and ultimate weakening (EEUW) is proposed in this paper. This approach comprises two main steps. Firstly, an image with extreme contrast can be achieved by applying grey prediction evolution algorithm (GPE), which is the first time that GPE is introduced into dual-histogram thresholding method to find the optimal segmentation threshold for accurate segmentation. Secondly, a pure gray image can be obtained through a fusion strategy based on the grayscale world assumption to achieve the ultimate weakening. Experiments conducted on three standard underwater image benchmark datasets validate that EEUW outperforms the 10 state-of-the-art methods in improving the contrast of underwater images.

关键词： Contrast balance Extreme enhancement grey prediction evolution algorithm Pure gray image Ultimate weakening Underwater image enhancement

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