Loop bat family algorithm (Loop BFA) for constrained optimization

Miodragović, Goran; Bulatović, Radovan R.

doi:10.1007/s12206-015-0730-9

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…Miodragović and Bulatović [ 72 ] modified BA by incorporating the loop search in the zone of solutions. In this work, the Lévy flight step was used in every bat family seeking a better solution until the problem constraints were satisfied.…”

Section: Bat Algorithm Variantsmentioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

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Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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Section: Bat Algorithm Variantsmentioning

confidence: 99%

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Shehab

Abu-Hashem

Shambour

et al. 2022

Arch Computat Methods Eng

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“…The paper compares the efficiency of three modified biologically inspired algorithms: the cyclic algorithm of the bat family (Loop BFA), the hybrid cuckoo search and the firefly algorithm (H-CS-FA) and the modified krill herd algorithm (MKH). The cyclic algorithm of the bat family (Loop BFA) represents a modification of the standard BA by including the loop search in the zone of solutions [2,19]. H-CS-FA represents a combination of two algorithms and it was used in the optimization of the dynamic quantities of the mechanism which is described in detail in [3,4].…”

Section: Introductionmentioning

confidence: 99%

The application of metaheuristic algorithms in multi-objective optimization of engineering problems

Miodragović¹,

Bošković²,

Bulatović³

2022

Engineering Today

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Comparative testing of three modified metaheuristic algorithms - the cyclic algorithm of the bat family (Loop BFA), the hybrid cuckoo search and the firefly algorithm (H-CS-FA) and the modified krill herd algorithm (MKH)was performed in the paper. The authors tested these algorithms in relation to original ones and the effectiveness of the modifications was confirmed for each individual algorithm. Here, the effectiveness of all three algorithms was verified on two engineering problems in the field of metal cutting - optimization of the body of the turning knife and optimization of the eccentric of the clamping tool. In both cases, multi-objective optimization with two objective functions was performed. By comparing the obtained optimization results as well as the speed of convergence, appropriate conclusions about the efficiency of the algorithms and recommendations for their application were made.

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“…Miodragović introduced the Bat family to expand to continuously repeat the process of finding the optimal solution by including a loop search in the solution area. For each bat in each family, perform a fine search according to Levy-flight to find an improved solution until the given constraints are met [14]. An improved adaptive bat algorithm (SABA) was proposed, which has adaptive step control and mutation mechanism.…”

Section: Introductionmentioning

confidence: 99%

Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators

Zhu¹,

Wang²,

Wang³

et al. 2020

IEEE Access

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Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. Bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.

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Loop bat family algorithm (Loop BFA) for constrained optimization

Cited by 8 publications

References 30 publications

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

The application of metaheuristic algorithms in multi-objective optimization of engineering problems

Data Clustering Method Based on Improved Bat Algorithm With Six Convergence Factors and Local Search Operators

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