A new biologically inspired optimization algorithm

Premaratne, Upeka; Samarabandu, Jagath; Sidhu, T.S.

doi:10.1109/iciinfs.2009.5429852

Cited by 59 publications

(21 citation statements)

References 9 publications

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“…That means, a plant will be selected to the next iteration only if its fitness value ðyÞ is greater than the threshold as defined by Eq. 17.112 (Premaratne et al 2009). …”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 93%

“…Depending on the nature of the parameter space each dimension of the seed can be bonded such that Eq. 17.111 holds (Premaratne et al 2009;Wang et al 2011):…”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 96%

“…The total quantity of seeds ðsÞ produced by any plant would be a function of the plant fitness function and the maximum number of seeds ðq max Þ as defined by Eq. 17.113 (Premaratne et al 2009):…”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 96%

“…The number of viable seeds ðs v Þ produced by a plant can be expressed as Eq. 17.115 (Premaratne et al 2009):…”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 96%

“…Paddy field algorithm (PFA) was recently proposed in Premaratne et al (2009). In general, situation of each individual seed or plant can be illustrated as vector X ¼ x 1 ; x 2 ; .…”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 99%

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Emerging Biology-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, a group of (more specifically 56 in total) emerging biology-based computational intelligence (CI) algorithms are introduced. We first, in Sect. 17.1, describe the organizational structure of this chapter. Then, from Sects. 17.2 to 17.57, each section is dedicated to a specific algorithm which falls within this category, respectively. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 17.58 closes this chapter.

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“…That means, a plant will be selected to the next iteration only if its fitness value ðyÞ is greater than the threshold as defined by Eq. 17.112 (Premaratne et al 2009). …”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 93%

“…Depending on the nature of the parameter space each dimension of the seed can be bonded such that Eq. 17.111 holds (Premaratne et al 2009;Wang et al 2011):…”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 96%

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 96%

“…The number of viable seeds ðs v Þ produced by a plant can be expressed as Eq. 17.115 (Premaratne et al 2009):…”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 96%

“…Paddy field algorithm (PFA) was recently proposed in Premaratne et al (2009). In general, situation of each individual seed or plant can be illustrated as vector X ¼ x 1 ; x 2 ; .…”

Section: Fundamentals Of Paddy Field Algorithmmentioning

confidence: 99%

See 3 more Smart Citations