Chaotic fruit fly optimization algorithm

Mitić, Marko; Vuković, Najdan; Petrović, Milica; Miljković, Zoran

doi:10.1016/j.knosys.2015.08.010

Cited by 173 publications

(73 citation statements)

References 22 publications

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“…The chaotic sequence generated from tent mapping model follows uniform distribution, but the value will quickly fall into the small cycle under the influence of word length. It is short of good randomness [18,19]. Using a model, which can produce uniform distribution chaotic sequence, to improve FOA individual distribution could increase the algorithm search efficiency.…”

Section: Improvements On the Random Distribution Of Fruit Flymentioning

confidence: 99%

An Improved Fruit Fly Optimization Algorithm and Its Application in Heat Exchange Fouling Ultrasonic Detection

Xia

Sun

et al. 2018

Mathematical Problems in Engineering

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Inspired by the basic theory of Fruit Fly Optimization Algorithm, in this paper, cat mapping was added to the original algorithm, and the individual distribution and evolution mechanism of fruit fly population were improved in order to increase the search speed and accuracy. The flowchart of the improved algorithm was drawn to show its procedure. Using classical test functions, simulation optimization results show that the improved algorithm has faster and more reliable optimization ability. The algorithm was then combined with sparse decomposition theory and used in processing fouling detection ultrasonic signals to verify the validity and practicability of the improved algorithm.

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Section: Improvements On the Random Distribution Of Fruit Flymentioning

confidence: 99%

An Improved Fruit Fly Optimization Algorithm and Its Application in Heat Exchange Fouling Ultrasonic Detection

Xia

Sun

et al. 2018

Mathematical Problems in Engineering

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“…To evaluate the performance of E-Jaya, twelve standardized benchmark functions 20 are employed. The functions are uni-modal and multi-modal.…”

Section: Benchmark Functionsmentioning

confidence: 99%

An Enhanced Jaya Algorithm with a Two Group Adaption

Gong¹

2017

IJCIS

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This paper proposes a novel performance enhanced Jaya algorithm with a two group adaption (E-Jaya). Two improvements are presented in E-Jaya. First, instead of using the best and the worst values in Jaya algorithm, EJaya separates all candidates into two groups: the better and the worse groups based on their fitness values, then the mean of the better group and the mean of the worse group are used. Second, in order to add non algorithm-specific parameters in E-Jaya, a novel adaptive method of dividing the two groups has been developed. Finally, twelve benchmark functions with different dimensionality, such as 40, 60, and 100, were evaluated using the proposed EJaya algorithm. The results show that E-Jaya significantly outperformed Jaya algorithm in terms of the solution accuracy. Additionally, E-Jaya was also compared with a differential evolution (DE), a self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. E-Jaya algorithm outperforms all the algorithms.

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“…The tested functions are widely used by researchers [34][35][36][37]. These functions are: Tables 11, 12 and 13 demonstrate that Multi-pop-ABC performs better than the compared ABC, PS-ABC and PS-ABCII algorithms [34][35][36] in terms of both mean and standard deviation (SD). Note that the best results are highlighted in bold.…”

Section: Comparison With State-of-the-art Approaches On Test Functionsmentioning

confidence: 99%

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Nseef

Abdullah

Turky

et al. 2016

Knowledge-Based Systems

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Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multipopulation artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC.Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results.

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Chaotic fruit fly optimization algorithm

Cited by 173 publications

References 22 publications

An Improved Fruit Fly Optimization Algorithm and Its Application in Heat Exchange Fouling Ultrasonic Detection

An Improved Fruit Fly Optimization Algorithm and Its Application in Heat Exchange Fouling Ultrasonic Detection

An Enhanced Jaya Algorithm with a Two Group Adaption

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

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