A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

Zhao, Futao; Yao, Zhong; Luan, Jing; Xing, Song

doi:10.1155/2016/2167413

Cited by 17 publications

(17 citation statements)

References 23 publications

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“…Hybridization in ant colony optimization using the cuckoo search [20], and using GA [21,22] are also developed for effective feature selection process. Mafarja and Mirjalili developed a cohesive method to combine simulated annealing methods with the whale optimization approach for identifying the ideal set of features [23].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Mallika

Selvamuthukumaran²

2021

Int J Comput Intell Syst

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Diabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a Crow Search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimization-based SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

Mallika

Selvamuthukumaran²

2021

Int J Comput Intell Syst

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“…In 2015, the PSO algorithm was enhanced employing some cognitive learning mechanisms for solving an optimization problem [39]. In 2016, the ant colony optimization algorithm (ACOA) was integrated with GA, and a new optimization algorithm was introduced [40]. A study used GA to discover the closest solution to the best one to deal with nonlinear multimodal optimization problems [41].…”

Section: Literaturementioning

confidence: 99%

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

et al. 2020

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Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

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“…Dorigo in 1992 and has been widely used in many areas [21][22][23][24][25][26][27][28][29][30][31][32], such as fuzzy predictive control, behavior learning and reproduction by robots, and mobile ad hoc network optimization. The general idea of ACA is to mimic the process of ants seeking an optimum path between their colony and a source of food.…”

Section: Ant Colony Algorithm Mechanism Ant Colony Algorithm (Aca) Imentioning

confidence: 99%

A New Mathematical Method for Solving Cuttings Transport Problem of Horizontal Wells: Ant Colony Algorithm

Liu

Heng-bin

et al. 2017

Mathematical Problems in Engineering

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Cuttings transport problem has long been recognized as one of the key difficulties in drilling horizontal wells, and the models in cuttings transport research are usually formulated with highly nonlinear equations set. When using Newton methods to solve real engineering problems with nonlinear equations set, the problems of result dependence on initial values, Jacobian matrix singularity, and variable outflow of its definition domain in iterations are three of the often-encountered difficulties. In this paper, the ant colony algorithm is applied to solve the two-layer cuttings transport model with highly nonlinear equations set. The solution-searching process of solving nonlinear equations set is transformed into an optimization process of searching the minimum value of an objective function by applying ant colony algorithm. Analyzing the results of the example, it can be concluded that ant colony algorithm can be used to solve the highly nonlinear cuttings transport model with good solution accuracy; transforming the solution-searching process of solving nonlinear equations set into an optimization process of searching the minimum value of the objective function is necessary; the real engineering problem should be simplified as much as possible to decrease the number of unknown variables and facilitate the use of ant colony algorithm.

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A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization

Cited by 17 publications

References 23 publications

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

A Hybrid Crow Search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

A New Mathematical Method for Solving Cuttings Transport Problem of Horizontal Wells: Ant Colony Algorithm

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