Measuring the curse of population size over swarm intelligence based algorithms

Dhal, Krishna Gopal; Das, Arunita; Sahoo, Samarendu; Das, Rohi; Das, Sanjoy

doi:10.1007/s12530-019-09318-0

Cited by 18 publications

(7 citation statements)

References 39 publications

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“…This section presents the experimental results which have been computed with the help of six NIOA and Masi entropy over satellite images. The six NIOA are Aquila Optimizer (AQO) (Abualigah et al 2021a ), Arithmetic Optimization Algorithm (AOA) (Abualigah et al 2021b ), Archimedes Optimization Algorithm (AROA) (Hashim et al 2021 ), Rat Swarm Optimization Algorithm (RSA) (Dhiman et al 2021 ), Particle Swarm Optimization (PSO) (Dhal et al 2019c ), and Firefly Algorithm (FA) (Dhal et al 2020d ). It can be noticed that four NIOA i.e., AQO, AOA, AROA, and RSA are developed in 2021 and they are very popular NIOA according to citation in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA.

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Section: Resultsmentioning

confidence: 99%

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

2022

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“…In the source paper of AOA, authors set the population size (n) to 30 for all tested benchmark functions and other problems. However, literature showed that a fixed population size for all kinds of problems over different dimensions cannot be scientific and thus, it is called the curse of population size [35,36]. According to the literature, one type of adaptation strategy to include well-balanced exploration and exploitation in any NIOA is to vary the population size.…”

Section: Parameter Sensitivitymentioning

confidence: 99%

A Comprehensive Survey on Arithmetic Optimization Algorithm

Dhal

Sasmal

Das

et al. 2023

Arch Computat Methods Eng

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Arithmetic Optimization Algorithm (AOA) is a recently developed population-based nature-inspired optimization algorithm (NIOA). AOA is designed under the inspiration of the distribution behavior of the main arithmetic operators in mathematics and hence, it also belongs to mathematics-inspired optimization algorithm (MIOA). MIOA is a powerful subset of NIOA and AOA is a proficient member of it. AOA is published in early 2021 and got a massive recognition from research fraternity due to its superior efficacy in different optimization fields. Therefore, this study presents an up-to-date survey on AOA, its variants, and applications.

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“…In order to balance exploration and exploitation phases of an algorithm, population size adaptation schemes can automatically adjust population size according to population diversity during the search process thus enhancing performance and reducing run time. Population size adaptation has been widely studied in genetic algorithms [ 5 , 25 ], differential evolution [ 40 , 50 ], artificial bee colony optimization [ 9 ], swarm intelligence [ 7 , 12 , 41 ] and recently to sine cosine algorithm [ 3 ]. However, to the best of the author’s knowledge, no such work has been reported for SMA.…”

Section: Introductionmentioning

confidence: 99%

FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm

Al-Fadhli

Jaragh

Alfailakawi

et al. 2022

Neural Comput & Applic

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In this paper, an adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA) is proposed. Unlike the original SMA where population size is fixed in every epoch, FP-SMA will adaptively change population size in order to effectively balance exploitation and exploration characteristics of SMA’s different phases. Experimental results on 13 standard and 30 IEEE CEC2014 benchmark functions have shown that FP-SMA can achieve significant reduction in run time while maintaining good solution quality when compared to the original SMA. Typical saving in terms of function evaluations for all benchmarks was between 20 and 30% on average with a maximum being as high as 60% in some cases. Therefore, with its higher computation efficiency, FP-SMA is much more favorable choice as compared to SMA in time stringent applications.

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Measuring the curse of population size over swarm intelligence based algorithms

Cited by 18 publications

References 39 publications

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

A Comprehensive Survey on Arithmetic Optimization Algorithm

FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm

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