An Backbone Guided Extremal Optimization Method for Solving the Hard Maximum Satisfiability Problem

Zeng, Guo‐Qiang; Chen, Zheng; Zhang, Zhengjiang; Lu, Yong-Zai

doi:10.2991/iccasm.2012.332

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Swarm intelligence algorithms are good at solving many optimization problems, such as traveling salesman problems [41], feature selection [42][43][44][45][46], object tracking [47,48], wind speed prediction [49], PID optimization control [50][51][52], image segmentation [53,54], the hard maximum satisfiability problem [55,56], parameter optimization [22,[57][58][59], gate resource allocation [60,61], fault diagnosis of rolling bearings [62,63], the detection of foreign fibers in cotton [64,65], large-scale supply chain network design [66], cloud workflow scheduling [67,68], neural network training [69], airline crew rostering problems [70], and energy vehicle dispatch [71]. This section conducts a qualitative analysis of MSMA.…”

Section: The Qualitative Analysis Of Msmamentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

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Section: The Qualitative Analysis Of Msmamentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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“…Hence, more attention should be paid to the accuracy and efficacy of the procedure used to tackle the model [ 21 , 22 ]. The swarm intelligence optimization algorithm has shown great potential in solving a multitude of practical problems, including but not limited to, detection of feature selection issues [ [23] , [24] , [25] ], parameter optimization [ [26] , [27] , [28] ], engineering problems [ [29] , [30] , [31] ], PID optimization control [ [32] , [33] , [34] ], prediction problems in educational field [ [35] , [36] , [37] ], the hard maximum satisfiability problem [ 38 , 39 ], foreign fiber in cotton [ 40 , 41 ], medical diagnosis [ [42] , [43] , [44] ], scheduling problem [ 45 , 46 ], wind speed prediction [ 47 ], bankruptcy prediction [ [48] , [49] , [50] ], fault diagnosis of rolling bearings [ 51 , 52 ], and gate resource allocation [ 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

Liu

Zhao

et al. 2021

Computers in Biology and Medicine

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This paper focuses on the study of multilevel COVID-19 X-ray T image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present the first ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was succefully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com .

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“…More and more Swarm Intelligence (SI) based algorithms have arisen in recent years [1,2] . SI-based algorithms can be a solution to many problems like medical diagnosis [3][4][5][6] , financial distress prediction [7][8][9] , energy field [10][11][12] , engineering problems [13][14][15][16][17][18][19] , feature reduction [20,21] , educational field [22][23][24] , maximum satisfiability problem [25,26] , PID optimization control [27][28][29] , wind speed prediction [30] , fault diagnosis of rolling bearings [31,32] , gate resource allocation [33,34] and scheduling problem [35,36] . SI based algorithms can be classified into two categories: the environment inspires one kind, such as Particle Swarm Optimization (PSO) [37] , Artificial Bee Colony (ABC) [38] , and so on; another kind is inspired by social behavior, for example, Moth-Flame Optimization (MFO) [39] , Harris Hawks Optimizer (HHO) [40] , Slime Mould Algorithm (SMA) [41] , etc.…”

Section: Introductionmentioning

confidence: 99%

Generalized Oppositional Moth Flame Optimization with Crossover Strategy: An Approach for Medical Diagnosis

Xia

Zhang

et al. 2021

J Bionic Eng

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In the original Moth-Flame Optimization (MFO), the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame, so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems. Therefore, in this work, a generalized oppositional MFO with crossover strategy, named GCMFO, is presented to overcome the mentioned defects. In the proposed GCMFO, GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate; crisscross search (CC) is adopted to promote the exploitation and/or exploration ability of MFO. The proposed algorithm’s performance is estimated by organizing a series of experiments; firstly, the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems. Secondly, GCMFO is applied to handle multilevel thresholding image segmentation problems. At last, GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases, including the appendicitis diagnosis, overweight statuses diagnosis, and thyroid cancer diagnosis. Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy. It also indicates that the presented GCMFO has a promising potential for application.

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An Backbone Guided Extremal Optimization Method for Solving the Hard Maximum Satisfiability Problem

Cited by 8 publications

References 20 publications

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

Generalized Oppositional Moth Flame Optimization with Crossover Strategy: An Approach for Medical Diagnosis

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