Swarm and Evolutionary Computation

JournalCitation impact & published research

Swarm and Evolutionary Computation is a journal indexed in ScholarIQ from OpenAlex. ScholarIQ records 2,253 works, 91,884 citations, an h-index of 129 and an APC (USD) of 3,580.

2,253

Works

91,884

Citations

129

h-index

3,580

APC (USD)

IDs:OpenAlex

What are the most-cited papers on Swarm and Evolutionary Computation?

ScholarIQmost cited works

An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis

Ashraf Darwish, Dalia Ezzat, Aboul Ella Hassanien

Swarm and Evolutionary Computation. 2019275 Citations

An improved cuckoo search based extreme learning machine for medical data classification

Puspanjali Mohapatra, Sujata Chakravarty, Priyabrata Dash

Swarm and Evolutionary Computation. 2015190 Citations

Gene selection for cancer types classification using novel hybrid metaheuristics approach

Alok Kumar Shukla, Pradeep Singh, Manu Vardhan

Swarm and Evolutionary Computation. 202083 Citations

An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering

Himanshu Mittal, Mukesh Saraswat

Swarm and Evolutionary Computation. 201862 Citations

Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning

Zhiping Tan, Yu Tang, Kangshun Li, Huasheng Huang, Shaoming Luo

Swarm and Evolutionary Computation. 202247 Citations

Related on ScholarIQ

An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis

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An improved cuckoo search based extreme learning machine for medical data classification

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Gene selection for cancer types classification using novel hybrid metaheuristics approach

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An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering

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Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning

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A two-stage evolutionary algorithm for large-scale sparse multiobjective optimization problems

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