Hybrid Bat Algorithm with Artificial Bee Colony

Nguyen, Trong-The; Pan, Jeng‐Shyang; Dao, Thi-Kien; Kuo, Mu-Yi; Horng, Mong-Fong

doi:10.1007/978-3-319-07773-4_5

Cited by 24 publications

(16 citation statements)

References 12 publications

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“…Yılmaz and [48] suggested three different methods to enhance the local and global search of BA, and they employed both standard test functions and constrained realworld problems to validate the performance of their approaches. Nguyen et al [49] hybridized BA with ABC algorithm and four benchmark functions are used to test the convergence of the proposed method. Pan et al [50] applied the concepts of parallel processing and communication strategy to hybrid PSO with BA and six benchmark functions are tested to validate the approached method.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel discrete bat algorithm for solving the travelling salesman problem

Saji

Riffi

2015

Neural Comput & Applic

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The travelling salesman problem (TSP) is one of the well-known NP-hard combinatorial optimization and extensively studied problems in discrete optimization. The bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. In this paper we extend a discrete bat-inspired algorithm to solve the famous TSP. Although many algorithms have been used to solve TSP, the main objective of this research is to investigate this discrete version to achieve significant improvements, not only compared to traditional algorithms but also to another metaheuristics. Moreover, this study is based on a benchmark dataset of symmetric TSP from TSPLIB library.Keywords Travelling salesman problem Á NP-hard combinatorial optimization problem Á Nature-inspired metaheuristic Á Discrete bat-inspired algorithm

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Section: Literature Reviewmentioning

confidence: 99%

A novel discrete bat algorithm for solving the travelling salesman problem

Saji

Riffi

2015

Neural Comput & Applic

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“…A. [21][22][23][24][25][26][27][28][29][30][31][32][33][34], and the random parameter characteristics are listed in Table 11.…”

Section: Example 4: Welded Beammentioning

confidence: 99%

Reliability-based design optimization using the directional bat algorithm

Chakri

Yang

Khelif

et al. 2017

Neural Comput & Applic

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Reliability based design optimization (RBDO) problems are important in engineering applications, but it is challenging to solve such problems. In this study, a new resolution method based on the directional Bat Algorithm (dBA) is presented. To overcome the difficulties in the evaluations of probabilistic constraints, the reliable design space concept has been applied to convert the yielded stochastic constrained optimization problem from the RBDO formulation into a deterministic constrained optimization problem. In addition, the constraint handling technique has also been introduced to the dBA so that the algorithm can solve constrained optimization problem effectively. The new method has been applied to several engineering problems and the results show that the new method can solve different varieties of RBDO problems efficiently. In fact, the obtained solutions are consistent with the best results in the literature. Citation details: A. Chakri, X.-S. Yang, R. Khelif, M. Becouaret, Reliability based design optimization using the directional bat algorithm, Neural Computing and Applications, First published online, 2017. https://doi.org/10.1007/s00521-016-2797-3 3the time delay between the two ears, they can create a 3D mental image of their surrounding and determine if there is food or not. This behavior was the basic idea that has been used to develop the bat algorithm. Several studies showed that BA can solve optimization problems with higher efficiency compared to standard algorithms such as .Despite the fact that BA is a powerful optimization algorithm, it may suffer from the premature convergence that can occur under certain conditions, which is also true for all other algorithms such as PSO and GA. To overcome this problem, several techniques have been proposed to increase the exploitation and exploration capability of the algorithm. In [23], the authors proposed to use simulated annealing and Gaussian perturbation to speed up the convergence rate. In [24], the authors suggested to use chaotic maps to control the pulse rate and loudness. In [25], the authors recommended to use the Lévy flights and the differential operator to generate the bats ' movements and, in [26], the authors proposed to consider the bats' habitat selection and their self-adaptive compensation for the Doppler effect in the algorithm formulation. Other studies suggested hybridization between the standard BA and classical algorithm such as PSO [27], Artificial Bee Colony (ABC) [28], differential evolution [29,30] and Invasive Weed Optimization (IWO) [31].

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“…The evolved bat algorithm (EBA) is used for numerical optimization and the economic load dispatch problem [4,5]. A hybrid between the BA and the artificial bee colony (ABC) is used for solving numerical optimization problems [6]. Several discrete BAs have been proposed in the literature, in addition to the continuous BAs.…”

Section: Introductionmentioning

confidence: 99%

A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks

2019

Self Cite

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Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of available devices. This paper proposes a novel optimization algorithm, namely the compact bat algorithm (cBA), to use for the class of optimization problems involving devices which have limited hardware resources. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for the solution of the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems. Experimental results demonstrate that the proposed algorithm achieves an effective way to use limited memory devices and provides competitive results.

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Hybrid Bat Algorithm with Artificial Bee Colony

Cited by 24 publications

References 12 publications

A novel discrete bat algorithm for solving the travelling salesman problem

A novel discrete bat algorithm for solving the travelling salesman problem

Reliability-based design optimization using the directional bat algorithm

A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks

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