Cuckoo Search Optimization- A Review

Joshi, Ashok; Kulkarni, Omkar; Kakandikar, Ganesh M.; Nandedkar, Vilas M.

doi:10.1016/j.matpr.2017.07.055

Cited by 160 publications

(121 citation statements)

References 14 publications

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…The fitness value of a food source represents the quality of the associated solution. The Cuckoo Search (CS) [28] algorithm for image contrast enhancement is reported in [8]. The CS algorithm is based on the parasitic breeding behavior of the cuckoo bird.…”

Section: Literature Surveymentioning

confidence: 99%

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

et al. 2020

View full text Add to dashboard Cite

Image contrast enhancement is a very important phase for processing of digital images. The main goal of image contrast enhancement is to improve the visual quality by improving the contrast level of images which were distorted or degraded due to casual acquisition of images. The most popular method to perform this task is Histogram Equalization (HE). However, the exhaustive approach taken during HE is an algorithmically complex task. In this paper, we have considered image contrast enhancement as an optimization problem, where a new meta-heuristic algorithm, called Barnacles Mating Optimizer (BMO) is used to find the optimal solution for this optimization problem. A grey level mapping technique is used here to convert an image to a solution of the optimization problem. The algorithm has been evaluated on five publicly available datasets: Kodak, MIT-Adobe FiveK images, H-DIBCO 2016, and H-DIBCO 2018. It is also applied on some standard images like Boy, Lena, Lifting body and Zebra. The obtained results clearly display the effectiveness of the proposed method. The results obtained on the Kodak images are compared with many state-of-the-art methods present in the literature, and the comparison proves the superiority of the proposed method. To test the applicability of BMO in solving real world problems, we have applied it as a pre-processing step in binarization of H

show abstract

Section: Literature Surveymentioning

confidence: 99%

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Details on the parameter settings of the biofuel supply chain model used in this work could be obtained in Tan et al [61]. Joshi et al [33]). It was initially inspired by brood parasitism which was often found among certain species of cuckoo birds.…”

Section: +Cmentioning

confidence: 99%

Random Matrix Generators for Optimizing a Fuzzy Biofuel Supply Chain System

Ganesan¹,

Vasant

Sanghvi³

et al. 2020

J. ADV. ENG. COMPUT.

View full text Add to dashboard Cite

Complex industrial systems often contain various uncertainties. Hence sophisticated fuzzy optimization (metaheuristics) techniques have become commonplace; and are currently indispensable for effective design, maintenance and operations of such systems. Unfortunately, such state-of-the-art techniques suffer several drawbacks when applied to largescale problems. In line of improving the performance of metaheuristics in those, this work proposes the fuzzy random matrix theory (RMT) as an add-on to the cuckoo search (CS) technique for solving the fuzzy large-scale multiobjective (MO) optimization problem; biofuel supply chain. The fuzzy biofuel supply chain problem accounts for uncertainties resulting from fluctuations in the annual electricity generation output of the biomass power plant [kWh/year]. The details of these investigations are presented and analyzed.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

show abstract

“…Based on the above, in this paper, the roulette selection mechanism was replaced with the tournament selection. Inspired by Cuckoo Search Optimization (CS) [40,41], Flower Pollination Algorithm (FPA) [42,43] and Martingale Algorithm (DA) [44], LMVO has introduced the concept of Lévy flight into white hole/black update. Based on this, a better algorithm model is obtained by adding a mutation factor in this paper [37].…”

Section: The Proposed Multilevel Thresholding Algorithmmentioning

confidence: 99%

“…FPA, inspired by the process of flower pollination of flowering plants in nature which is simple and requires fewer parameters to be adjusted [41,42]; 6.…”

mentioning

confidence: 99%

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

et al. 2019

View full text Add to dashboard Cite

A novel multilevel threshold segmentation method for color satellite images based on Masi entropy is proposed in this paper. Lévy multiverse optimization algorithm (LMVO) has a strong advantage over the traditional multiverse optimization algorithm (MVO) in finding the optimal solution for the segmentation in the three channels of an RGB image. As the work advancement introduces a Lévy multiverse optimization algorithm which uses tournament selection instead of roulette wheel selection, and updates some formulas in the algorithm with mutation factor. Then, the proposal is called TLMVO, and another advantage is that the population diversity of the algorithm in the latest iterations is maintained. The Masi entropy is used as an application and combined with the improved TLMVO algorithm for satellite color image segmentation. Masi entropy combines the additivity of Renyi entropy and the non-extensibility of Tsallis entropy. By increasing the number of thesholds, the quality of segmenttion becomes better, then the dimensionality of the problem also increases. Fitness function value, average CPU running time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) were used to evaluate the segmentation results. Further statistical evaluation was given by Wilcoxon's rank sum test and Friedman test. The experimental results show that the TLMVO algorithm has wide adaptability to high-dimensional optimization problems, and has obvious advantages in objective function value, image quality detection, convergence performance and robustness.

show abstract

Cuckoo Search Optimization- A Review

Cited by 160 publications

References 14 publications

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

Random Matrix Generators for Optimizing a Fuzzy Biofuel Supply Chain System

Masi Entropy for Satellite Color Image Segmentation Using Tournament-Based Lévy Multiverse Optimization Algorithm

Contact Info

Product

Resources

About