Cuckoo search: State-of-the-art and opportunities

Yang, Xin‐She; Deb, Suash

doi:10.1109/iscmi.2017.8279597

Cited by 14 publications

(7 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another research direction is to consider other QoS such as fault tolerance and load balancing to cope with unpredictable failures and achieve global stable equilibrium. Furthermore, it would also be interesting to investigate other metaheuristics such as GA, 54 PSO, 55 or CS 56 as well as their hybridization with FPA algorithm to analyze and compare the behavior of the task scheduling process on the achieved global performances.…”

Section: Discussionmentioning

confidence: 99%

Multi‐objective task scheduling in cloud computing

Malti

Hakem

Benmammar

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary Cloud computing services are used to fulfill user requests, often expressed in the form of tasks and their execution in such environments requires efficient scheduling strategies that take into account both algorithmic and architectural characteristics. Unfortunately, this problem is known to be NP‐hard in its general form. Despite the fact that several studies have been published in the literature, there are still interesting and relevant questions to be addressed. Indeed, most of the previous studies focus on a single objective and in the case where they deal with a set of objectives, they use a simple compromise function and do not consider how each of the parameters might influence the others. To this end, we propose an efficient task scheduling algorithm which is based on the pollination behavior of flowers and makes use of both Pareto optimality principle and TOPSIS technique to improve the quality of the obtained solutions. Both single and multiobjective optimization variants are investigated. In the latter case, three optimization criteria are considered, namely, minimizing the time makespan or schedule length, the execution cost, and maximizing the overall reliability of the task mapping. Different test‐bed scenarios and QoS metrics were considered and the obtained results corroborate the merits of the proposed algorithm.

show abstract

Section: Discussionmentioning

confidence: 99%

Multi‐objective task scheduling in cloud computing

Malti

Hakem

Benmammar

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In consideration with various existing metaheuristic methods, cuckoo search (CS) presented by Yang and Deb has appeared to be one of the effective soft computing techniques [ 33 – 37 ]. Recently, CS is widely accepted by maximum number of researchers in solving numerous optimization tasks [ 38 , 39 ].…”

Section: Computer-based Diagnosis Methodsmentioning

confidence: 99%

“…Normally, the parameter “ α ” is assigned with a positive integer (i.e., α > 0) and in this research, “ α ” is allocated as 1. Additional particulars regarding CS are cited in the works of Yang and Deb [ 37 ].…”

Section: Computer-based Diagnosis Methodsmentioning

confidence: 99%

Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm

Manic

Biju

Patel

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The work proposes a computer-based diagnosis method (CBDM) to delineate and assess the corpus callosum (CC) segment from the 2-dimensional (2D) brain magnetic resonance images (MRI). The proposed CBDM consists of two parts: (1) preprocessing and (2) postprocessing sections. The preprocessing tools have a multithreshold technique with the chaotic cuckoo search (CCS) algorithm and a preferred threshold procedure. The postprocessing employs a delineation process for extracting the CC section. The proposed CBDM finally extracts the vital CC parameters, such as total brain area (TBA) and CC area (CCA) to classify the considered 2D MRI slices into the control and autism spectrum disorder (ASD) groups. This attempt considers the benchmark brain MRI database which includes ABIDE and MIDAS for the experimental investigation. The results obtained with ABIDE dataset are further confirmed against the fuzzy C -means driven level set (FCM + LS) and multiphase level set (MLS) technique and the proposed CBDM with Shannon entropy along with active contour (SE + AC) presented improved result in comparison to the existing methodologies. Further, the performance of CBDM is confirmed on MIDAS and clinical dataset. The experimental outcomes approve that the proposed CBDM extracts the CC section from the 2D MR brain images that have higher accuracy compared to alternative techniques.

show abstract

“…The year-wise development of the algorithm is presented with the parameters of different application areas and its contribution to improving the performance. 33,34 The article is organized in the following way: First, the necessary working procedure of CS search algorithm is discussed in detail. The next section explains how it is incorporated in robot path planning, after that the next section deals with the details of proposed work, describing the use of tournament function and the CS algorithm.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Optimized cuckoo search algorithm using tournament selection function for robot path planning

Sharma

Singh

Doriya

2021

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

show abstract

Cuckoo search: State-of-the-art and opportunities

Cited by 14 publications

References 90 publications

Multi‐objective task scheduling in cloud computing

Multi‐objective task scheduling in cloud computing

Extraction and Evaluation of Corpus Callosum from 2D Brain MRI Slice: A Study with Cuckoo Search Algorithm

Optimized cuckoo search algorithm using tournament selection function for robot path planning

Contact Info

Product

Resources

About