Hybrid Differential Artificial Bee Colony Algorithm

Abraham, Ajith; Jatoth, Ravi Kumar; Anguluri, Rajasekhar

doi:10.1166/jctn.2012.2019

Cited by 86 publications

(28 citation statements)

References 12 publications

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“…El-Abd [2011a] explored the use of opposition-based learning in ABC. Abraham et al [2012] introduced as others a type of DE strategy into the search equation of ABC. Some other variants emphasized on modifications in search equations of ABC steps.…”

Section: E22 Variants Of the Artificial Bee Colony Algorithmmentioning

confidence: 99%

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Liao¹

2015

4OR-Q J Oper Res

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ii SummaryContinuous optimization problems are optimization problems where all variables have a domain that typically is a subset of the real numbers; mixed discretecontinuous optimization problems have additionally other types of variables, so that some variables are continuous and others are on an ordinal or categorical scale. Continuous and mixed discrete-continuous problems have a wide range of applications in disciplines such as computer science, mechanical or electrical engineering, economics and bioinformatics. These problems are also often hard to solve due to their inherent difficulties such as a large number of variables, many local optima or other factors making problems hard. Therefore, in this thesis our focus is on the design, engineering and configuration of high-performing heuristic optimization algorithms.We tackle continuous and mixed discrete-continuous optimization problems with two classes of population-based heuristic algorithms, ant colony optimization (ACO) algorithms and evolution strategies. In a nutshell, the main contributions of this thesis are that (i) we advance the design and engineering of ACO algorithms to algorithms that are competitive or superior to recent state-of-the-art algorithms for continuous and mixed discrete-continuous optimization problems, (ii) we improve upon a specific state-of-the-art evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), and (iii) we extend CMA-ES to tackle mixed discrete-continuous optimization problems.More in detail, we propose a unified ant colony optimization (ACO) framework for continuous optimization (UACOR). This framework synthesizes algorithmic components of two ACO algorithms that have been proposed in the literature and an incremental ACO algorithm with local search for continuous optimization, which we have proposed during my doctoral research. The design of UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new, high-performing ACO algorithms for continuous optimization. We also propose iCMAES-ILS, a hybrid algorithm that loosely couples IPOP-CMA-ES, a CMA-ES variant that uses a restart schema coupled with an increasing population iii size, and a new iterated local search (ILS) algorithm for continuous optimization. The hybrid algorithm consists of an initial competition phase, in which IPOP-CMA-ES and the ILS algorithm compete for further deployment during a second phase. A cooperative aspect of the hybrid algorithm is implemented in the form of some limited information exchange from IPOP-CMA-ES to the ILS algorithm during the initial phase. Experimental studies on recent benchmark functions suites show that UACOR and iCMAES-ILS are competitive or superior to other state-of-the-art algorithms.To tackle mixed discrete-continuous optimization problems, we extend ACO MV and propose CES MV , an ant colony optimization algorithm and a covariance matrix adaptation evolution strategy, respectively. In ACO MV and CES MV , the decision variables of an optimization prob...

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Section: E22 Variants Of the Artificial Bee Colony Algorithmmentioning

confidence: 99%

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Liao¹

2015

4OR-Q J Oper Res

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“…Honey bees are social bugs and demonstrate features like bee rummaging, bee ballet, crowned head bee, chore collection, cooperative decision building, shell site choice, copulating, pheromone setting and steering systems, used as the replica of intellectual solicitations. It is simple, easy to employ, highly amenable, widely used by many researchers for various optimization problems [6]. This paper outlines methods and materials in section II, the proposed MFCM-ABC is described in Section III, Section IV presents the results and discussion, Section V concludes the paper.…”

Section: Introductionmentioning

confidence: 99%

Brain Tissue Classification using PCA with Hybrid Clustering Algorithms

Karuna¹,

Saladi²,

Bhattacharyya³

2018

IJET

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Distinct algorithms were developed to segment the MRI images, to satisfy the accuracy in segmenting the regions of the brain. In this paper, we proposed a novel methodology for segmenting the MRI brain images using the clustering techniques. The Modified Fuzzy CMeans (MFCM) algorithm is pooled with the Artificial Bee Colony (ABC) algorithm after denoising images, features are extracted using Principal Component Analysis (PCA) for better results of segmentation. This improves the ability to extract the regions (cluster centres) and cells in the normal and abnormal brain MRI images. The comparative analysis of proposed methodology with existing FCM, ABC algorithms is evaluated in terms of Minkowski score. The proposed MFCM-ABC method is more robust and efficient to hostile noise in images when compared to existing FCM and ABC methods.

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“…To overcome these pitfalls, so me research papers have introduced modifications to the classical A BC algorith m in order to imp rove its performance and tackle more complex real-world problems [31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Handling Fuzzy Image Clustering with a Modified ABC Algorithm

Ouadfel¹,

Meshoul²

2012

IJISA

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Abstract-Image segmentation can be cast as a clustering task where the image is partitioned into clusters. Pixels within the same cluster are as homogenous as possible whereas pixels belonging to different clusters are not similar in terms of an appropriate similarity measure. Several clustering methods have been proposed for image seg mentation purpose among wh ich the Fu zzy C-Means clustering algorith m. However this algorith m still suffers fro m some drawbacks, such as local optima and sensitivity to init ialization. Art ificial Bees Co lony algorith m is a recent population-based optimization method which has been successfully used in many complex problems. In this paper, we propose a new fuzzy clustering algorithm based on a modified Artificial Bees Colony algorith m, in which a new mutation strategy inspired from the Differential Evolution is introduced in order to improv e the explo itation process. Experimental results show that our proposed approach imp roves the performance o f the basic fuzzy C-Means clustering algorith m and outperforms other population based optimization methods.

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Hybrid Differential Artificial Bee Colony Algorithm

Cited by 86 publications

References 12 publications

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Brain Tissue Classification using PCA with Hybrid Clustering Algorithms

Handling Fuzzy Image Clustering with a Modified ABC Algorithm

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