Bat algorithm for constrained optimization tasks

Gandomi, Amir H.; Yang, Xin-She; Alavi, Amir H.; Talatahari, Siamak

doi:10.1007/s00521-012-1028-9

Cited by 475 publications

(181 citation statements)

References 61 publications

Supporting

Mentioning

176

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, the survival process of bats is achieved based on the injective (one-to-one) strategy, where the fit one replaces the worst one based on feasibility rule. The performance of Convergence behavior for LKP 21 Convergence behavior for LKP 22 Convergence behavior for LKP 23 Convergence behavior for LKP 24 Convergence behavior for LKP 25 Convergence behavior for LKP 26 Convergence behavior for LKP 26 Convergence behavior for LKP 27 Convergence behavior for LKP 28 Convergence behavior for LKP 29 Convergence behavior for LKP 30 Convergence behavior for LKP 31 Table 9 Wilcoxon test for comparison results in Tables 5 and 6 Compared methods Solution evaluations…”

Section: Discussionmentioning

confidence: 99%

“…The sizes of these instances include 100, 200, 300, 500, 700, 1000, 1200, 1500, 1800, 2000, 2600 and 3000 items. Each large-scale KP (KP 11 -KP 22 ) is generated as follows: the volume of each item is randomly chosen from 0.5 and 2 and its corresponding profit is randomly set between 0.5 and 1. The maximal volume capacity of the knapsack is limited to 0.75 times of the sum volumes of the items generated following the above procedure.…”

Section: Large-scale 0-1 Knapsack Problemsmentioning

confidence: 99%

“…The ability of such echolocation of micro-bats is charming, as these bats can find their prey and distinguish different types of insects even in complete darkness [20]. The earlier applications showed that BA could solve different optimization problems and proved that its efficiency and robustness compared to different algorithms such as GA and PSO [20][21][22]. A new trend in bat algorithms is focusing on hybridizing BA with different strategies [23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New binary bat algorithm for solving 0–1 knapsack problem

Rizk‐Allah

Hassanien

2017

Complex Intell. Syst.

View full text Add to dashboard Cite

This paper presents a novel binary bat algorithm (NBBA) to solve 0-1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm enables the bats to enhance the exploration capability while LSS aims to boost the exploitation tendencies and, therefore, it can prevent the BBA-LSS from the entrapment in the local optima. Moreover, the LSS starts its search from BBA found so far. By this methodology, the BBA-LSS enhances the diversity of bats and improves the convergence performance. The proposed algorithm is tested on different size instances from the literature. Computational experiments show that the BBA-LSS can be promise alternative for solving large-scale 0-1 knapsack problems.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Large-scale 0-1 Knapsack Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New binary bat algorithm for solving 0–1 knapsack problem

Rizk‐Allah

Hassanien

2017

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…One such algorithm is Bat algorithm which has diverse applications [33]. In [6] a simple Bat algorithm was used to solve constrained based optimization problem. A novel hybrid algorithm was designed for global numerical optimization by using Bat algorithm and Harmonic Search (HS) [27] .…”

Section: Motivation and Related Workmentioning

confidence: 99%

A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data

Doreswamy¹,

M²

2016

IJSCAI

View full text Add to dashboard Cite

Advancement in information and technology has made a major impact on medical science where the researchers come up with new ideas for improving the classification rate of various diseases. Breast cancer is one such disease killing large number of people around the world. Diagnosing the disease at its earliest instance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN is exploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitness function is used for error minimization. FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.

show abstract

“…Nowadays, the group of algorithms that belong within the category of swarm intelligence is very extensive. Among the latest nature-inspired metaheuristics are the Bat Algorithm [8] the Harmony Search [16], the Firefly Algorithm [7,23], the Cuckoo Search [6,27], the Flower Pollination Algorithm [22] and the Krill Herd Algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

Training Neural Networks with Krill Herd Algorithm

Kowalski

źUkasik

2015

Neural Process Lett

View full text Add to dashboard Cite

In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that of the Krill Herd Algorithm (KHA) procedure. It contains many interesting mechanisms. The purpose of this article is to compare the KHA optimization algorithm used for learning an artificial neural network (ANN), with other heuristic methods and with more conventional procedures. The proposed ANN training method has been verified for the classification task. For that purpose benchmark examples drawn from the UCI Machine Learning Repository were employed with Classification Error and Sum of Square Errors being used as evaluation criteria. It has been concluded that the application of KHA offers promising performance-both in terms of aforementioned metrics, as well as time needed for ANN training.

show abstract

Bat algorithm for constrained optimization tasks

Cited by 475 publications

References 61 publications

New binary bat algorithm for solving 0–1 knapsack problem

New binary bat algorithm for solving 0–1 knapsack problem

A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data

Training Neural Networks with Krill Herd Algorithm

Contact Info

Product

Resources

About