A modified artificial bee colony algorithm

Gao, Weifeng; Liu, Sanyang

doi:10.1016/j.cor.2011.06.007

Cited by 539 publications

(236 citation statements)

References 12 publications

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“…The original ABC is well known for its exploration but poor at exploitation [10], [11]. This is mainly because of the following three reasons found in the search equation (2).…”

Section: The Proposed Hybrid Abcdementioning

confidence: 99%

“…Thus many techniques have been employed to enhance the convergence and performance reliability of ABC. Some of them include local search [5], Tagushi method [6], orthogonal learning strategy [11], multi-strategy ensemble [12]. Kang et al's Rosenbrock ABC algorithm [13] used a modified Rosenbrock's rotational direction method to implement the exploitation phase to assist ABC in solving complex problems.…”

Section: B Improved Variants Of Abcmentioning

confidence: 99%

“…Unfortunately, they are contradict to each other and both must be well balanced during the search for good performance. The original ABC is well known for its exploration but poor at exploitation [10], [11]. Thus many techniques have been employed to enhance the convergence and performance reliability of ABC.…”

Section: B Improved Variants Of Abcmentioning

confidence: 99%

“…Inspired by PSO, the gbest-guided ABC exploits the information of global best solution into the search equation to improve the exploitation [10]. Inspired by DE/best/1, the modified ABC searches only around the best solution to improve the exploitation [11] in addition to an improved initialization with chaotic system and opposition-based learning. A new probability parameter is added for balancing of the exploration and exploitation.…”

Section: B Improved Variants Of Abcmentioning

confidence: 99%

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A Hybrid Artificial Bee Colony with Differential Evolution

Worasucheep¹

2015

IJMLC

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Abstract-Artificial bee colony (ABC) is a relatively new stochastic algorithm with competitive performance and minimal tuning parameter. This paper proposes a hybrid ABC algorithm with differential evolution (DE), but without additional parameters. DE is a well-known efficient evolutionary algorithm with proven records but its parameter setting is complicated. This proposed hybrid algorithm called ABCDE incorporates the powerful mutation strategies of DE into ABC, in order to increase convergence while diversity is not compromised. The performance of ABCDE is evaluated against both original ABC and opposition-based DE (ODE), a recent DE variant with high performance. The experiment uses twelve widely accepted non-linear benchmark functions with various characteristics, such as difficult landscape, multimodality, shift and rotation, to evaluate the ABCDE's performance on many complex functions. The experimental results demonstrate a superior performance of ABCDE against original ABC and ODE.

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“…The original ABC is well known for its exploration but poor at exploitation [10], [11]. This is mainly because of the following three reasons found in the search equation (2).…”

Section: The Proposed Hybrid Abcdementioning

confidence: 99%

Section: B Improved Variants Of Abcmentioning

confidence: 99%

Section: B Improved Variants Of Abcmentioning

confidence: 99%

Section: B Improved Variants Of Abcmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Artificial Bee Colony with Differential Evolution

Worasucheep¹

2015

IJMLC

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“…That study compared the proposed method with PSO optimization method, where the method of ABC-LSSVM has high convergence speed than PSO-LSSVM. However, the ABC method produces a local minimum parameter [7].…”

Section: Jurnal Ilmu Komputer Dan Informasi (Journal Of Computer Sciementioning

confidence: 99%

Least Squares Support Vector Machines Parameter Optimization Based on Improved Ant Colony Algorithm for Hepatitis Diagnosis

Husain

Arisa

Rahayu

et al. 2017

Jurnal Ilmu Komputer dan Informasi

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Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for the 80-20% training-testing partition. Keywords: Classification, Least Squares Support Vector Machines, Improved Ant Colony Algorithm, Local Fisher Discriminant Analysis, Hepatitis Disease. AbstrakBanyak metode klasifikasi yang mampu mendiagnosa seorang pasien mengidap penyakit Hepatitis, salah satunya adalah menggunakan metode klasifikasi Least Squares Support Vector Machines (LSSVM). Terdapat dua parameter yang sangat berpengaruh pada LSSVM yaitu parameter kernel dan parameter regularisasi. Penentuan parameter optimal tersebut harus diperhatikan untuk mendapatkan akurasi klasifikasi yang tinggi pada LSSVM. Penelitian ini mengusulkan metode optimasi Improved Ant Colony Algorithm (IACA) dalam penentuan parameter optimal LSSVM untuk mendiagnosa penyakit Hepatitis. IACA membuat penyimpanan solusi untuk menjaga rute dari keseluruhan semut. Solusi yang disimpan adalah nilai parameter LSSVM. Ada 3 tahapan utama pada penelitian ini yaitu, dimensi dataset Hepatitis direduksi menggunakan metode Local Fisher Discriminant Analysis (LFDA), kemudian parameter optimal LSSVM dicari dengan metode optimasi IACA menggunakan data training, setelah itu data testing diklasifikasikan menggunakan parameter optimal LSSVM. Hasil uji coba menunjukkan bahwa metode yang diusulkan menghasilkan nilai akurasi yang tinggi (93,7%) pada partisi 80-20% training dan testing.

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A review of applied methods of optimization in steel structures

Kazem¹,

Matinrad²

2021

ce papers

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Recent dazzling progress in computer science and stringent requirements of the design of “more optimized” buildings put forwards the study and research of the various optimization methods in the building sector. This study presents a comprehensive review of the state‐of‐the‐art and the most applicable algorithms applying computational optimization to Steel buildings and different steel design problems. Four different algorithms including Neural Network, Bee and Bat‐Inspired algorithm and Metaheuristic Optimization algorithm have been studied with emphasized on the application of these methods in steel structures. Algorithm Key points are reviewed with more details including configuration of the algorithms and control of building systems, besides particular attention on accuracy and capability of them to be converged as an optimized solution. From different convergence rate, number of unknown parameters and boundaries of solutions It can be concluded that in near future researches should be oriented towards improving the efficiency of the most effective search techniques and realistic approximation methods especially in large‐scale building optimization problems; and reducing time with increasing accuracy for such activities. Further effort should be required to quantify the robustness in optimal answers so as to improve building performance.

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A modified artificial bee colony algorithm

Cited by 539 publications

References 12 publications

A Hybrid Artificial Bee Colony with Differential Evolution

A Hybrid Artificial Bee Colony with Differential Evolution

Least Squares Support Vector Machines Parameter Optimization Based on Improved Ant Colony Algorithm for Hepatitis Diagnosis

A review of applied methods of optimization in steel structures

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