An Exhaustive Survey on Nature Inspired Optimization Algorithms

Dixit, Manish; Silakari, Sanjay

doi:10.21742/ijdcasd.2014.1.1.02

Cited by 10 publications

(13 citation statements)

References 13 publications

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“…The experimental evaluation is conducted in two sections. Table 1 describes the first set (S 1 ) of benchmark functions consisting of (F 1 − F 17 ) functions [27] along with their definitions, range of features, global minimum fitness, optimal position values, and categories. Moreover, to validate the robustness, the second set (S 2 ) contains (C 1 − C 28 ) real-parameter single-objective unconstrained optimization functions [78] which are briefed in Table 2.…”

Section: Resultsmentioning

confidence: 99%

“…where N, k, and t correspond to the number of feature vectors, number of clusters, and number of iterations respectively. Table 1 The considered standard benchmark functions [27] S.No. Ackley…”

Section: Time Complexity Of Kigsa-c Methodsmentioning

confidence: 99%

“…Generally, metaheuristic methods have exhibited good performance on numerous nonlinear and complex optimization problems [24][25][26]. From the last two decades, they have been an integral part of many real-world applications ranging from design to business planning [27,28]. Some popular metaheuristic methods are genetic algorithm [29], differential algorithm [30], particle swarm optimization [31], ant colony optimization [32], biogeography-based optimizer [33], and artificial bee colony [34].…”

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confidence: 99%

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A new clustering method for the diagnosis of CoVID19 using medical images

et al. 2021

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With the spread of COVID-19, there is an urgent need for a fast and reliable diagnostic aid. For the same, literature has witnessed that medical imaging plays a vital role, and tools using supervised methods have promising results. However, the limited size of medical images for diagnosis of CoVID19 may impact the generalization of such supervised methods. To alleviate this, a new clustering method is presented. In this method, a novel variant of a gravitational search algorithm is employed for obtaining optimal clusters. To validate the performance of the proposed variant, a comparative analysis among recent metaheuristic algorithms is conducted. The experimental study includes two sets of benchmark functions, namely standard functions and CEC2013 functions, belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value, Friedman test, and box-plot. Further, the presented clustering method tested against three different types of publicly available CoVID19 medical images, namely X-ray, CT scan, and Ultrasound images. Experiments demonstrate that the proposed method is comparatively outperforming in terms of accuracy, precision, sensitivity, specificity, and F1-score. Keywords CoVID19 diagnosis • Clustering • Metaheuristic algorithm • Gravitational search algorithm 1 Introduction With the rapid outbreak of the Corona virus Disease (CoVID19) around the globe [1], the World health organization (WHO) has been declared CoVID19 as a Public Health Emergency of International Concern (PHEIC) [2]. Pathogenically, the virus identified in CoVID19 is "severe acute respiratory syndrome coronavirus 2" (SARS-CoV-2) which is quite distinct from other respiratory viruses like MERS-CoV, SARS-CoV, avian influenza, and influenza [3]. Usual symptoms in a COVID19 patient are fever, dry cough, and tiredness. Other signs experienced by patients include sore throat, diarrhea, headache, loss of taste

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Section: Resultsmentioning

confidence: 99%

Section: Time Complexity Of Kigsa-c Methodsmentioning

confidence: 99%

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confidence: 99%

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A new clustering method for the diagnosis of CoVID19 using medical images

et al. 2021

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“…From 2010 till now there are numerous algorithms have been proposed in the literature that are surveyed in many papers like [61]- [71]. Each of these algorithms have its own pros and cons, however, due to lack of space we only mention the common drawbacks between them: they mostly need great effort to utilize, they have complicated parts or details therefore it is hard to understand, inefficient number of iteration and lack of optimum coverage time.…”

Section: Brief History Of Evolutionary Algorithms and Related Workmentioning

confidence: 99%

Raccoon Optimization Algorithm

et al. 2019

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Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the raccoon optimization algorithm (ROA). The ROA is inspired by the rummaging behaviors of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviors exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA's ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other wellknown optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time. INDEX TERMS Raccoon optimization algorithm (ROA), nonlinear continuous optimization problems, structural optimization, evolutionary algorithm, meta-heuristic algorithm. NOMENCLATURE

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“…There are few swarm intelligence survey papers available in literature which provides theoretical description followed by algorithms of swarm intelligence techniques like bat algorithm, ant colony optimization, particle swarm optimization, and so on. Most of these algorithms are old and they are discussed from biological optimization point of view, they lack application and performance point of view description [7][8][9]. Swarm artificial intelligence techniques which are developed recently includes ageist spider monkey, shark smell optimization, whale optimization, lion optimization, spider, antlion optimization, jellyfish food search, eagle search, elephant, raven roosting, dragonfly, crow search and others.…”

Section: Introductionmentioning

confidence: 99%

Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based

Bhargavi¹,

Babu²,

Pitt

2020

Journal of Intelligent Systems

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AbstractCloud computing deals with voluminous heterogeneous data, and there is a need to effectively distribute the load across clusters of nodes to achieve optimal performance in terms of resource usage, throughput, response time, reliability, fault tolerance, and so on. The swarm intelligence methodologies use artificial intelligence to solve computationally challenging problems like load balancing, scheduling, and resource allocation at finite time intervals. In literature, sufficient works are being carried out to address load balancing problem in the cloud using traditional swarm intelligence techniques like ant colony optimization, particle swarm optimization, cuckoo search, bat optimization, and so on. But the traditional swarm intelligence techniques have issues with respect to convergence rate, arriving at the global optimum solution, complexity in implementation and scalability, which limits the applicability of such techniques in cloud domain. In this paper, we look into performance modeling aspects of some of the recent competitive swarm artificial intelligence based techniques like the whale, spider, dragonfly, and raven which are used for load balancing in the cloud. The results and analysis are presented over performance metrics such as total execution time, response time, resource utilization rate, and throughput achieved, and it is found that the performance of the raven roosting algorithm is high compared to other techniques.

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An Exhaustive Survey on Nature Inspired Optimization Algorithms

Cited by 10 publications

References 13 publications

A new clustering method for the diagnosis of CoVID19 using medical images

A new clustering method for the diagnosis of CoVID19 using medical images

Raccoon Optimization Algorithm

Performance Modeling of Load Balancing Techniques in Cloud: Some of the Recent Competitive Swarm Artificial Intelligence-based

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