Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of *** only is spam wasting users’time and *** addition,it limits the stora...
详细信息
Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of *** only is spam wasting users’time and *** addition,it limits the storage space of the email box as well as the disk ***,spam detection is a challenge for individuals and organizations *** advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and ***,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this ***,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.
In surface mining, blast-induced dust can be discharged to the atmosphere and impact the surrounding environment and nearby residential areas, especially if a large volume of rock is blasted under inappropriate meteor...
详细信息
In surface mining, blast-induced dust can be discharged to the atmosphere and impact the surrounding environment and nearby residential areas, especially if a large volume of rock is blasted under inappropriate meteorological conditions such as high wind speed. Many attempts have been done to predict the blast-induced dust emission distance but the literature of the dust reduction is limited to change stemming materials based on water capsules. This study develops a methodology using gene expression programming and grasshopper optimization algorithm to find an optimal blasting plan with minimum blast-induced dust in a mine close to sensitive ecosystem and residential areas. The best gene expression programming model, which indicates relationship between dependent and independent variables, was first determined based on 100 blasting data collected from the mine. The model with the R-2 of 0.9559 and 0.9145, respectively, for training and validating parts was chosen as the best model. The model, as an objective function, was considered in grasshopper optimization algorithm to find the optimal blasting plan with minimum dust emission level. Compared to the old blasting plans of the mine, the optimal plan resulted in a reduction of 76.82% in the emission distance of the blast-induced under constant meteorological conditions. Sensitivity analysis on the system parameters revealed the high sensitivity of the output to wind speed, air temperature, air humidity, powder factor, and stemming. [GRAPHICS] .
The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and *** optimizationalgorithm(GOA)is a fresh population b...
详细信息
The estimation of the fuzzy membership function parameters for interval type 2 fuzzy logic system(IT2-FLS)is a challenging task in the presence of uncertainty and *** optimizationalgorithm(GOA)is a fresh population based meta-heuristic algorithm that mimics the swarming behavior of grasshoppers in nature,which has good convergence ability towards *** main objective of this paper is to apply GOA to estimate the optimal parameters of the Gaussian membership function in an *** antecedent part parameters(Gaussian membership function parameters)are encoded as a population of artificial swarm of grasshoppers and optimized using its *** of the consequent part parameters are accomplished using extreme learning *** optimized IT2-FLS(GOAIT2FELM)obtained the optimal premise parameters based on tuned consequent part parameters and is then applied on the Australian national electricity market data for the forecasting of electricity loads and *** forecasting performance of the proposed model is compared with other population-based optimized IT2-FLS including genetic algorithm and artificial bee colony optimization *** of the performance,on the same data-sets,reveals that the proposed GOAIT2FELM could be a better approach for improving the accuracy of the IT2-FLS as compared to other variants of the optimized IT2-FLS.
Optimizing real-life engineering design problems are challenging and somewhat difficult if optimum solutions are expected. The development of new efficient optimizationalgorithms is crucial for this task. In this pap...
详细信息
Optimizing real-life engineering design problems are challenging and somewhat difficult if optimum solutions are expected. The development of new efficient optimizationalgorithms is crucial for this task. In this paper, a recently invented grasshopper optimization algorithm is upgraded from its original version. The method is improved by adding an elite opposition-based learning methodology to an elite opposition-based learning grasshopper optimization algorithm. The new optimizer, which is elite opposition-based learning grasshopperoptimization method (EOBL-GOA), is validated with several engineering design probles such as a welded beam design problem, car side crash problem, multiple clutch disc problem, hydrostatic thrust bearing problem, three-bar truss, and cantilever beam problem, and finally used for the optimization of a suspension arm of the vehicles. The optimum results reveal that the EOBL-GOA is among the best algorithms reported in the literature.
作者:
Li, QiWang, JunmuSu, GuoshaoGuangxi Polytech Construct
Sch Civil Engn Nanning 530007 Peoples R China Guangxi Univ
Coll Civil Engn & Architecture Key Lab Disaster Prevent & Struct Safety Minist Educ Nanning 530004 Peoples R China Guangxi Univ
Guangxi Prov Engn Res Ctr Water Secur & Intellige Nanning 530004 Peoples R China
Non-probabilistic reliability analysis has great developmental potential in the field of structural reliability analysis, as it is often difficult to obtain enough samples to construct an accurate probability distribu...
详细信息
Non-probabilistic reliability analysis has great developmental potential in the field of structural reliability analysis, as it is often difficult to obtain enough samples to construct an accurate probability distribution function of random variables based on probabilistic theory. In practical engineering cases, the performance function (PF) is commonly implicit. Monte Carlo simulation (MCS) is commonly used for structural reliability analysis with implicit PFs. However, MCS requires the calculation of thousands of PF values. Such calculation could be time-consuming when the structural systems are complicated, and numerical analysis procedures such as the finite element method have to be adopted to obtain the PF values. To address this issue, this paper presents a grasshopper optimization algorithm-based response surface method (RSM). First, the method employs a quadratic polynomial to approximate the implicit PF with a small set of the actual values of the implicit PF. Second, the grasshopper optimization algorithm (GOA) is used to search for the global optimal solution of the scaling factor of the convex set since the problem of solving the reliability index is transformed into an unconstrained optimal problem. During the search process in the GOA, a dynamic response surface updating strategy is used to improve the approximate accuracy near the current optimal point to improve the computing efficiency. Two mathematical examples and two engineering structure examples that use the proposed method are given to verify its feasibility. The results compare favorably with those of MCS. The proposed method can be non-invasively combined with finite element analysis software to solve non-probabilistic reliability analysis problems of structures with implicit PF with high efficiency and high accuracy.
grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimi...
详细信息
grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimizationalgorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimizationalgorithms.
Structural weight design is essential and difficult in engineering structure optimization. The design is affected by many factors and belongs to the NP problem. Swarm intelligent algorithm provides a valid way to solv...
详细信息
Structural weight design is essential and difficult in engineering structure optimization. The design is affected by many factors and belongs to the NP problem. Swarm intelligent algorithm provides a valid way to solve the NP problem. grasshopper optimization algorithm (GOA) is a nature-inspired algorithm that mimics the swarming behaviors of grasshopper insects, but the original GOA has two main problems: the convergence rate is slow and the convergence accuracy is poor. We propose a novel grasshopper optimization algorithm (CV-GOA) consisting of chaos strategy and velocity perturbation mechanism to improve the performance of standard GOA. In CV-GOA, the initial artificial swarm is constructed by Logistic map to increase the diversity of the population and improve the feasibility of finding the global optimal solution;then a set of the velocity vector is introduced and the velocity perturbation mechanism is used to update the velocity of grasshoppers and disturbs the position of grasshoppers, it can improve the searching speed of the algorithm and help the algorithm jump out of the local optimal trap, and improve the optimization accuracy of the algorithm. Experiments are conducted on fifteen benchmark functions to test the accuracy and convergence rate of CV-GOA. Experiments show the proposed CV-GOA achieves higher precision and better convergence rate than other variants. In addition, three structural weight design problems are optimized by CV-GOA, they are cantilever beam design problem, pressure vessel design problem and speed reducer design problem. The results indicate structural weight is designed with superiority. It also proves the effectiveness and value of the proposed algorithm.
A growing number of qualified ophthalmologists are promoting the need to use computer-based retinal eye processing image recognition technologies. There are different methods and algorithms in retinal images for detec...
详细信息
A growing number of qualified ophthalmologists are promoting the need to use computer-based retinal eye processing image recognition technologies. There are different methods and algorithms in retinal images for detecting optic discs. Much attention has been paid in recent years using intelligent algorithms. In this paper, in the human retinal images, we used the grasshopper optimization algorithm to implement a new automated method for detecting an optic disc. The clever algorithm is influenced by the social nature of the grasshopper, the intelligent grasshopperalgorithm. Include this algorithm;the population contains the grasshoppers, each of which has a common luminance or exercise score. In this method, two-by-two insects are compared, so it could be shown that less attractive insects shift towards more attractive insects. Finally, one of the most attractive insects is selected, and this insect gives an optimum solution to the problem. Here, we used the light intensity of the retinal pixels instead of grasshopper illuminations. According to local variations, the effect of these insects also indicates different light intensity values in images. Since the brightest area "represents the optic disc in retinal images, all insects travel to the brightest area, which leads to the determined position for an optic disc in the image. The performance was evaluated on 210 images, reflecting three Open to the public and sequentially distributed datasets DIARETDB1 89 images, STARE 81 images, and DRIVE 40 images. The results of the proposed algorithm implementation give a 99.51% accuracy rate in the DiaRetDB1 dataset, 99.67% in the STARE dataset, and 99.62% in the DRIVE dataset. The results of the implementation show the strong capacity and accuracy of the proposed algorithm for detecting the optic disc from retinal images. Also, the recorded time required for (OD) detection in these images is180.14 s for the DiaRetDB1, 65.13s for STARE, and 80.64s for DRIVE, respectively. Th
To provide effective resolutions for complex real-life problems and other optimization problems, abundant, various procedures have been presented in the last few decades. This paper proposes a simple but efficient hyb...
详细信息
To provide effective resolutions for complex real-life problems and other optimization problems, abundant, various procedures have been presented in the last few decades. This paper proposes a simple but efficient hybrid evolutionary algorithm called GOA-DE for solving visual tracking problems. In the proposed hybrid algorithm, grasshopper optimization algorithm (GOA) operates in refining the vector. In contrast, the Differential Evolution (DE) algorithm is used for transforming the decision vectors based on genetic operators. The improvement in maintaining the balance between exploration and exploitation abilities is made by incorporating genetic operators, namely, mutation and crossover in GOA. The success of GOA-DE is estimated by 23 classical benchmark functions, CEC05 functions, and CEC 2014 functions. The GOA-DE algorithm results prove that it is very viable associated with the metaheuristic up-to-date procedures. Similarly, visual tracking problems are resolved by the GOA-DE algorithm as a real challenging case study. Visual tracking several objects robustly in a video stream with complex backgrounds and objects are beneficial in subsequent generation computer vision structures. But, in exercise, it is problematic to plan an effective video-based visual tracking scheme owing to the fast-moving objects, probable occlusions, and diverse light circumstances. Investigational outcomes indicate that the GOA-DE-based tracker can energetically track a random target in many thought-provoking cases.
As one of the latest meta-heuristic algorithms, the grasshopper optimization algorithm (GOA) has extensive applications because of its efficiency and simplicity. However, the basic GOA still has enough room for improv...
详细信息
As one of the latest meta-heuristic algorithms, the grasshopper optimization algorithm (GOA) has extensive applications because of its efficiency and simplicity. However, the basic GOA still has enough room for improvement. Therefore, a new variant GOA algorithm which combines two strategies, namely PCA-GOA, is proposed. Firstly, principal component analysis strategy is employed to obtain the grasshoppers with minimally correlated variables, which can improve the exploitation capability of the GOA. Then, a novel inertia weight is proposed to balance exploration and exploitation in an intelligent way, which makes the GOA to have better search capability. Furthermore, the performance of PCA-GOA is evaluated by solving a series of benchmark functions. The experimental results manifest that the PCA-GOA provides better outcomes than the basic GOA and other state-of-the-art algorithms on the majority of functions, which demonstrates the superiority of the PCA-GOA.
暂无评论