Improved crow search algorithm based on arithmetic crossover—a novel metaheuristic technique for solving engineering optimization problems

Kumar, Sachin; Fred, A. Lenin; Miriam, L. R. Jonisha; Gulyás, Balázs; H, Ajay Kumar; Dayana, Nisha

doi:10.1016/b978-0-12-823799-1.00011-5

Cited by 5 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khattab et al [68] developed a novel crow spiral-based search algorithm to solve the design problem formulated, and the gained results confirmed the success of the filter design. Kumar et al [69] designed a hybrid CSA with an arithmetic crossover to two real-world engineering optimization problems and gained effective results. Li et al designed [70] an improved CSA with an extreme learning machine model to effectively forecast short-term wind power.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The superiority of the algorithms proposed in [2,25,29] was confirmed based on different well-known test systems and a set of benchmark test functions (BTFs). The arithmetic crossover concept in GA was incorporated into CSA to classify stages of brain tumors in [49]. Compared with the classical particle swarm optimization (PSO) and GA, CSA was found to be robust for standard BTFs.…”

Section: Introductionmentioning

confidence: 99%

“…GA, on the other hand, is a metaheuristic algorithm highly applicable in solving such a complex problem. Thus far, it has been considered to adjust the position of the crows after a flight with the help of only one genetic operatorcrossover [45,49]. The proposed GA-CSA hybrid fully integrates GA into CSA's initial group strategy, aiming to distribute the initial crows' population more uniformly and in a more focused manner.…”

Section: Introductionmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

View full text Add to dashboard Cite

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

show abstract

“…In a short amount of time, metaheuristic algorithms can nd successful (near-optimal) solutions to di cult optimization problems. Metaheuristics are a type of estimate optimization algorithm that can help you get away from the local optimum and can be used in engineering problems [19]. Metaheuristic algorithms have recently been developed in many categories.…”

Section: Introductionmentioning

confidence: 99%

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Alweshah

Jebril

Alkhalaileh

et al. 2022

Preprint

View full text Add to dashboard Cite

Feature selection (FS) is an important preprocessing step that has been commonly used in several fields to improve the performance of learning algorithms. In the field of medical data mining, a huge number of features are used in diagnosing disease, but these features have a lot of non-relevant weak correlations and redundant characteristics, which causes a number of problems that adversely affect diagnostic predictive accuracy. Work on FS has grown extensively many fields due to increased demand for methods that can reduce the dimensionality of data by choosing the best subset of features according to specific criteria in order to maximize prediction accuracy and minimize irrelevant features. In recent times, metaheuristics have been preferred over conventional optimization methods for solving FS problems in order to try to obtain an almost optimal solution in a finite time. Metaheuristics are general-purpose algorithms that can be used to solve almost any optimization problem because they generate “appropriate” solutions in a reasonable amount of time, which is especially useful when seeking to solve complicated problems. Many popular implementations have shown the utility of metaheuristics in different ways by contrasting their performance on well-known problems with that of other algorithms or applications .There are many metaheuristic algorithms in the literature such as those based on swarm intelligence, including particle swarm optimizations and ant colony optimization. The major objective of this research is to provide an increased degree of accuracy to resolve FS problems by conducting different experiments using a metaheuristic algorithm, namely the heap-based optimizer algorithm (HBO). The HBO is used with a k-nearest neighbor classifier in a wrapper to improve the FS process. The performance of the proposed method is evaluated and compared against seven approaches in the literature that are applied on nine high-dimensional data sets that contain, a low number of samples and multiple classes. The findings reveal that the HBO decreases the number of features for classification tasks, and is able to achieve high accuracy in two data sets as compared to the other approaches, the BHBO achieved the best convergence speed as compared to the competing methods. It is therefore concluded that the proposed HBO method can be used to optimize the FS process, whether in terms of classification accuracy or selection size.

show abstract

Improved crow search algorithm based on arithmetic crossover—a novel metaheuristic technique for solving engineering optimization problems

Cited by 5 publications

References 30 publications

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Heap Based Optimizer Algorithm for Solving Feature Selection Problems in High-Dimensional Cancer Microarray Data

Contact Info

Product

Resources

About