Biogeography-based optimisation with chaos

Saremi, Shahrzad; Mirjalili, Seyedali; Lewis, Andrew

doi:10.1007/s00521-014-1597-x

Cited by 313 publications

(136 citation statements)

References 38 publications

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“…CS and CCS are tested 25 times for each function, respectively. The fraction probability of CS is 0.25, while the Cycle map and the Gauss map, whose initial values are 0.7 similarly done in [23,26], are used to define the scaling factor and the fraction probability , respectively. Table 6 shows Error of two algorithms at different dimensions.…”

Section: Effect Of Chaotic Maps On Csmentioning

confidence: 99%

“…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%

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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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Section: Effect Of Chaotic Maps On Csmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cuckoo Search Algorithm with Chaotic Maps

Wang

Zhong

2015

Mathematical Problems in Engineering

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“…Simon [25] recently proposed the biogeography-based optimization (BBO) inspired by biogeography, which is one of the most promising algorithms to solve some challenging real problems. Saremi et al [23] introduced the chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities.…”

Section: Introductionmentioning

confidence: 99%

A grouping biogeography-based optimization for location area planning

Kim

Byeon²,

Lee

et al. 2015

Neural Comput & Applic

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Location area planning (LAP) is a combinatorial optimization grouping problem for the cellular mobile network. We propose a novel grouping biogeographybased optimization (GBBO), which has suitable migration and mutation with generating good initial habitats to partition the optimal number of location areas. The migration is to move the whole cells of location area (LA) with a randomly selected cell between habitats for emigration and immigration, while the adjacent cell mutation is carried out between LAs within one habitat. These group migration and mutation mechanisms are available to maintain the grouping conditions. This proposed GBBO helps us to obtain the optimal number of location areas and the corresponding configuration of the partitioned network. We also illustrate the GBBO approach using the small, medium, and large size problems to compare with artificial bee colony, particle swarm optimization, and previous LAP methods. The experimental results show that our novel GBBO is robust to find the best configurations of LAP with much less computation time comparing with other considered methods.

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“…Due to the ergodicity and dynamic properties of chaos, chaotic maps can help meta-heuristic optimization algorithms to enhance the diversity among individuals and avoid premature convergence. In the literature, chaotic maps have been incorporated into evolutionary algorithms [16], particle swarm optimization [17], biogeography-based optimisation [18], water cycle algorithm [19], fruit fly optimization [20], Krill Herd algorithm [21], bat algorithm [22], differential evolution algorithm [23], harmony search algorithm [24], firefly algorithm [25], ant swarm optimization [26], imperialist competitive algorithm [27], and others.…”

Section: Introductionmentioning

confidence: 99%

Multiple Chaos Embedded Gravitational Search Algorithm

Song

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different chaotic maps with a manner of local search. The embedded chaotic local search can exploit a small region to refine solutions obtained by the canonical gravitational search algorithm (GSA) due to its inherent local exploitation ability. Meanwhile it also has a chance to explore a huge search space by taking advantages of the ergodicity of chaos. To fully utilize the dynamic properties of chaos, we propose three kinds of embedding strategies. The multiple chaotic maps are randomly, parallelly, or memory-selectively incorporated into GSA, respectively. To evaluate the effectiveness and efficiency of the proposed MCGSA, we compare it with GSA and twelve variants of chaotic GSA which use only a certain chaotic map on a set of 48 benchmark optimization functions. Experimental results show that MCGSA performs better than its competitors in terms of convergence speed and solution accuracy. In addition, statistical analysis based on Friedman test indicates that the parallelly embedding strategy is the most effective for improving the performance of GSA.

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Biogeography-based optimisation with chaos

Cited by 313 publications

References 38 publications

Cuckoo Search Algorithm with Chaotic Maps

Cuckoo Search Algorithm with Chaotic Maps

A grouping biogeography-based optimization for location area planning

Multiple Chaos Embedded Gravitational Search Algorithm

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