Integrating Chaos to Biogeography-Based Optimization Algorithm

Saremi, Shahrzad; Mirjalili, Seyedali

doi:10.7763/ijcce.2013.v2.268

Cited by 19 publications

(13 citation statements)

References 20 publications

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“…Note that this method of integrating chaotic maps in the selection phase of BBO is identical to that of [30]. It can be inferred from equation (3.1) that the chaotic maps are responsible for choosing the origin of immigration in our proposed chaotic selection operator.…”

Section: Chaotic Maps For Selectionmentioning

confidence: 98%

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Biogeography-based optimisation with chaos

Saremi

Mirjalili

Lewis

2014

Neural Comput & Applic

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302

110

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The Biogeography-Based Optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study we utilise 10 chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed Chaotic BBO (CBBO) algorithms are benchmarked on 10 test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition the results show that the combination of chaotic selection and emigration operators results in the highest performance.

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Section: Chaotic Maps For Selectionmentioning

confidence: 98%

“…Currently, there are few works in the literature for improving exploration and exploitation capabilities of BBO using chaos theory. In 2013, we integrated 3 chaotic maps with BBO and tested it over 4 test functions [30]. We showed that the chaotic maps can improve the performance of BBO.…”

Section: Introductionmentioning

confidence: 99%

Biogeography-based optimisation with chaos

Saremi

Mirjalili

Lewis

2014

Neural Comput & Applic

Self Cite

302

110

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“…Chaos theory is related to the study of chaotic dynamical systems that are highly sensitive to the initial conditions [23]. Recently, chaos theory has been integrated into genetic algorithm [24], differential evolution [25], firefly algorithm [26], krill herd [27,28], and biogeography-based optimization [23,29], and these have shown the effectiveness and efficiency of chaos theory. In light of the above investigations, we propose chaotic cuckoo search algorithm, called CCS, which utilizes chaotic maps to define the scaling factor and the fraction probability.…”

Section: Introductionmentioning

confidence: 99%

Cuckoo Search Algorithm with Chaotic Maps

Wang

Zhong

2015

Mathematical Problems in Engineering

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Cuckoo search algorithm is a novel nature-inspired optimization technique based on the obligate brood parasitic behavior of some cuckoo species. It iteratively employs Lévy flights random walk with a scaling factor and biased/selective random walk with a fraction probability. Unfortunately, these two parameters are used in constant value schema, resulting in a problem sensitive to solution quality and convergence speed. In this paper, we proposed a variable value schema cuckoo search algorithm with chaotic maps, called CCS. In CCS, chaotic maps are utilized to, respectively, define the scaling factor and the fraction probability to enhance the solution quality and convergence speed. Extensive experiments with different chaotic maps demonstrate the improvement in efficiency and effectiveness.

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“…To obtain increased levels of population diversity, chaotic mapping of emigration and mutation operators of the BBO algorithm is pursued in achieving better optimization levels. Numerous chaotic maps have been used in metaheuristic algorithms to improve their results through proper balancing between exploration and exploitation activities (Giri et al, 2017, Guo-ping et al, 2016, Saremi and Mirjalili, 2013, Saremi et al, 2014. Jalili et al (2014) used chaotic BBO to resolve the truss structure puzzle which consists of several local optima.…”

Section: Introductionmentioning

confidence: 99%

Superior Chaotic Bbo for Optimization Problems

2020

Ethics and Information Technology

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Integrating Chaos to Biogeography-Based Optimization Algorithm

Cited by 19 publications

References 20 publications

Biogeography-based optimisation with chaos

Biogeography-based optimisation with chaos

Cuckoo Search Algorithm with Chaotic Maps

Superior Chaotic Bbo for Optimization Problems

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