Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding

Brajević, Ivona; Tuba, Milan

doi:10.1007/978-3-319-02141-6_6

Cited by 53 publications

(29 citation statements)

References 47 publications

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“…Results for CS and FA are from [49]. All tests were done on an Intel Core i7-3770K @3.5 GHz with 16 GB of RAM running under the Windows 8 x64 operating system.…”

Section: Resultsmentioning

confidence: 99%

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Improved Bat Algorithm Applied to Multilevel Image Thresholding

Alihodžić

Tuba

2014

The Scientific World Journal

Self Cite

126

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Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.

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“…Results for CS and FA are from [49]. All tests were done on an Intel Core i7-3770K @3.5 GHz with 16 GB of RAM running under the Windows 8 x64 operating system.…”

Section: Resultsmentioning

confidence: 99%

“…The population size in all algorithms was set to N = 40 and the number of generation is set to G = 2000 for all algorithms, as in [49]. Besides these common control parameters, each of mentioned algorithms has additional control parameters that directly improve their performance.…”

Section: Resultsmentioning

confidence: 99%

Improved Bat Algorithm Applied to Multilevel Image Thresholding

Alihodžić

Tuba

2014

The Scientific World Journal

Self Cite

126

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“…firefly algorithm also involved for (49,50). Apart of all the above firefly algorithm (51). Along with this firefly along with cuckoo participated for (52).…”

Section: B Applications Areas Of Fire Fly Optimizationmentioning

confidence: 99%

A Review of Bio Inspired Computing and its Applications

kumar¹,

Bhargava²

2018

J.JBTW

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Bio Inspired computation is the part of Artificial intelligence which was inspired by the biological behaviors of biological systems. Swarm intelligence is the collective behavior of an organized group in day-to-day life. Common examples of swarm intelligence include ant colony, bee colony, etc. and some are non-swarm intelligence like bat algorithm, etc. This study mainly focuses on application areas of various bio inspired computing based swarm and non-swarm intelligence. This review also discusses the newly developed algorithms. Specific application areas of such algorithms have been discussed in this research. This research highlighted the future scope of present algorithms.

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“…Cuckoo search (CS) simulates search process by utilizing Levy flights. It was proved to be a robust optimization technique [6]. Seeker optimization algorithm (SOA) models human search procedure that employs human reasoning, memory, interactions, and past experience.…”

Section: Introductionmentioning

confidence: 99%

Upgraded Firefly Algorithm for Portfolio Optimization Problem

Tuba

Bačanin

2014

2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation

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Portfolio selection is a well-known intractable research problem in the area of economics and finance. There are many definitions of the problem that by introduction of additional constraints try to make it closer to the realword conditions. Firefly algorithm is one of the latest swarm intelligence metaheuristics that was very successfully applied to both, unconstrained and constrained hard optimization problems. In this paper we adjusted firefly algorithm to the portfolio optimization problem and since the results were not completely satisfactory, we modified it so that better exploitation/exploration balance was achieved. We tested our improved algorithm on unconstrained portfolio problem, as well as on the problem formulation with cardinality and bounding constraints. We used official benchmark data sets from the OR-Library, and included data from Hang Seng in Hong Kong, DAX 100 in Germany and FTSE 100 in UK with 31, 85 and 89 assets respectively. Our upgraded algorithm proved to be uniformly better than the original one. Additionally, we compared it on the same data set to five other optimization metaheuristics from the literature and our upgraded firefly algorithm was better in most cases measured by all performance indicators.

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Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding

Cited by 53 publications

References 47 publications

Improved Bat Algorithm Applied to Multilevel Image Thresholding

Improved Bat Algorithm Applied to Multilevel Image Thresholding

A Review of Bio Inspired Computing and its Applications

Upgraded Firefly Algorithm for Portfolio Optimization Problem

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