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multiverse optimization algorithm Based on Levy Flight Improvement for Multithreshold Color Image Segmentation

IEEE ACCESS 2019年 7卷 32805-32844页

作者： Jia, Heming Peng, Xiaoxu Song, Wenlong Lang, Chunbo Xing, Zhikai Sun, Kangjian Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

Multithreshold segmentation is an indispensable part of modern image processing. Color images contain more information than gray images, therefore RGB multi-thresholding segmentation techniques have been drawn much attention during recent years. multiverse optimization (MVO) algorithm has a strong advantage in finding the optimal solution of three channels for RGB. In this paper, an MVO algorithm based on Levy flight (LMVO) is proposed. Levy flight is an efficient strategy which can not only increase the population diversity to prevent premature convergence but also improve the ability to jump out of the local optimum. Therefore, LMVO conduces to achieve a better balance between exploration and exploitation of MVO, so that it is faster and more robust than MVO and avoids premature convergence. Further LMVO algorithm is compared with the other eight famous meta-heuristics algorithms, by maximizing the objective function of Kapur's entropy method or of Otsu method to determine the optimal threshold. The maximum objective function, peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), CPU calculation time, optimal threshold value, and Wilcoxon's rank-sum test are used to evaluate the quality of the segmented image. The experimental results show that this method has obvious advantages in terms of objective function value, image quality measurement, convergence performance, and robustness.

关键词： multiverse optimization algorithm multithreshold segmentation Levy flight Kapur's entropy Otsu method

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Improved multiverse optimization algorithm for Fuzzy Flexible Job-Shop Scheduling Problem

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IEEE ACCESS 2023年 11卷 48259-48275页

作者： Fang, Jin-Cheng Zeng, A-Feng Zheng, Shao-Feng Zhao, Wen-Di He, Xu Fujian Univ Technol Coll Management Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Coll Transportat Intelligent Transportat Syst Res Ctr Fuzhou 350118 Peoples R China

An improved multiverse optimization (IMVO) algorithm is proposed herein for the fuzzy flexible job-shop scheduling problem pertaining to the non-deterministic polynomial-time hard (NP-hard) problem. First, we designed a hybrid initialization method to improve the quality of the initial solution, and thereafter, introduced a self-crossing technique along with an insert-based heuristic algorithm to simulate the process of exchanging objects between black/white holes and wormholes, respectively. Second, a universe selection mechanism is proposed to reduce the possibility of the algorithm falling into a local optimum. Third, four kinds of neighborhood structures were designed to improve the local search ability of the algorithm. In the decoding operation, we adopted the shift-left strategy to completely utilize the idle time of the machine. Finally, numerous experiments were conducted on the three benchmark test sets of various types and sizes to investigate the performance of the proposed IMVO algorithm. The experimental results demonstrated the effectiveness of the algorithm, especially in large-scale instances, displaying a strong superiority with an average maximum enhancement efficiency of 50.44%.

关键词： Fuzzy flexible job-shop scheduling problem multiverse optimization algorithm variable neighborhood search self-crossing technique universe selection mechanism

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Hybrid multiverse optimization algorithm With Gravitational Search algorithm for Multithreshold Color Image Segmentation

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IEEE ACCESS 2019年 7卷 44903-44927页

作者： Jia, Heming Peng, Xiaoxu Song, Wenlong Lang, Chunbo Xing, Zhikai Sun, Kangjian Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

Multithreshold segmentation of color images is an important part of modern image processing. Multithreshold segmentation techniques that use the ideas of optimization in mathematics to process the information contained in the three channels of RGB images have received much attention in recent years. In this paper, a hybrid algorithm of multiverse optimization algorithm with a gravitational search algorithm (GSMVO) is proposed. On the one hand, the gravitational mechanism among individuals in the universe enables particles to share information and move toward particles with large mass (the optimal solution), thus optimizing local optimization. On the other hand, the problem of a slow interpopulation update is improved. On the premise of improving the optimization precision, the convergence speed of the whole system is accelerated and the robustness can be improved. The time complexity of a proposed hybrid algorithm is analyzed. The quality of a segmented image is evaluated by using peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and Wilcoxon test. The experimental results illustrate that the proposed method has obvious advantages in objective function value, image quality measurement, convergence performance, and robustness.

关键词： multiverse optimization algorithm gravitational search algorithm multithreshold segmentation Kapur's entropy Otsu method

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Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Levy multiverse optimization algorithm

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REMOTE SENSING 2019年第8期11卷 942-942页

作者： Jia, Heming Peng, Xiaoxu Song, Wenlong Oliva, Diego Lang, Chunbo Li, Yao Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China Univ Guadalajara Dept Ciencias Computac CUCEI Ave Revoluc 1500 Guadalajara 44430 Jalisco Mexico

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Levy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Levy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

关键词： multilevel threshold segmentation Masi entropy multiverse optimization algorithm Levy multiverse optimization algorithm tournament selection

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A normalized deep neural network with self-attention mechanisms based multi-objective multi-verse optimization algorithm for economic dispatch

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APPLIED ENERGY 2025年 383卷

作者： Yin, Linfei Liu, Rongkun Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Guangxi Peoples R China

Traditional economic dispatch (ED) methods suffer from high costs, high carbon dioxide (CO2) emissions, and slow calculation speeds. Therefore, finding a new ED method that can effectively reduce costs and environmental pollution, and improve computational speed, is crucial. This study proposes a normalized deep neural network with a self-attention mechanism based multi-objective multi-verse optimization algorithm (NDNN-SAMMOMVO). NDNN-SAM-MOMVO combines deep neural network and multi-objective multiverse optimization (MOMVO) with the introduction of self-attention mechanism and layer normalization networks. In this study, NDNN-SAM-MOMVO is simulated in IEEE 118-, IEEE 2869-, and 11,476-bus systems;the performance of NDNNSAM-MOMVO is contrasted with other algorithms. Simulation results show that: (1) reducing costs and CO2 emissions;the proposed NDNN-SAM-MOMVO reduces the cost by 2.81 % and 1.14 % and CO2 emissions by 2.81 % and 0.63 % over MOMVO in these two systems, respectfully;(2) accelerating computational efficiency, the proposed NDNN-SAM-MOMVO saves 24.95 % and 20.33 % time over MOMVO in these two systems, respectively;(3) Euclidean distance performance metrics reflect the superb performance of the proposed NDNN-SAMMOMVO.

关键词： Multi-objective economic dispatch Self-attention mechanism Layer normalization Deep neural network multiverse optimization algorithm

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Auto-regulated radial base function structure implementation in the hybrid and ensemble hybrid domains to assess the hardened properties of novel mixture high performance concrete

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STRUCTURAL CONCRETE 2024年第5期25卷 3804-3823页

作者： Lei, Yang Tangshan Polytech Coll Sch Architectural Engn Tangshan Peoples R China Tangshan Polytech Coll Sch Architecture & Engn Tangshan 063299 Peoples R China

The mechanical properties of concrete, such as compressive strength CS$$ \left(\mathrm{CS}\right) $$ and slump flow SL$$ \left(\mathrm{SL}\right) $$ rates, are very nonlinear. For academics, it is crucial to forecast these qualities while creating new building methods. Such capabilities should be developed to lower the cost of expensive tests and increase the precision of the measurements. The goal of this study is to create an radial basis function neural network RBFNN$$ \left(\mathrm{RBFNN}\right) $$ to describe the characteristics of hardness in high-performance concrete (HPC) mix. Metaheuristic techniques were used to enhance the RBFNN's functionality. A dataset of 181 HPC mixes comprising ecologically beneficial ingredients, such as fly ash and silica fume, was used for training and evaluating the capabilities of the proposed hybrid models. According to the modeling process based on sensitivity analysis of input parameters and the results of hybrid models, the model combined with the multiverse optimization algorithm (MVO) had a higher correlation between the predicted and observed CS and slump values than the model combined with three optimization algorithms in terms of the R2 index being the maximum value of 0.984 in the tasting phase of CS and SL estimation. While evaluating two mechanical aspects of HPC samples, the RMSE$$ RMSE $$ of the model coupled with the MVO algorithm reconfirmed its accuracy being 3.59.

关键词： compressive strength concrete hardness high-performance concrete multiverse optimization algorithm neural network slump flow

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Estimation of compressive strength and slump of HPC concrete using neural network coupling with metaheuristic algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 577-591页

作者： Li, Wenqiao Wang, Ruijie Ai, Qisheng Liu, Qian Lu, Shu Xian Geophys Explorat Brigade Hubei Geol Bur Wuhan Hubei Peoples R China Liaoning Tech Univ Fuxin City Liaoning Peoples R China

The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements' accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) and multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R-2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples.

关键词： Compressive strength slump flow multiverse optimization algorithm concrete hardness neural network

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A novel membrane-inspired multiverse optimizer algorithm for quality of service-aware cloud web service composition with service level agreements

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第9期36卷 e5483-e5483页

作者： Maroosi, Ali Muniyandi, Ravie Chandren Univ Torbat Heydarieh Dept Comp Engn Torbat Heydarieh Iran Natl Univ Malaysia Fac Informat Sci & Technol Res Ctr Cyber Secur Bangi Selangor Malaysia

Cloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP-hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane-inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane-inspired approach to introduce a conventional membrane-inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane-inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth-flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO.

关键词： cloud computing membrane-inspired algorithm multiverse optimization algorithm service level agreement web service composition

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