Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

Salehan, Alireza; Deldari, Arash

doi:10.1007/s11227-021-04100-z

Cited by 27 publications

(15 citation statements)

References 86 publications

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“…( 2016 ) 135 Consultant Guide Search (CGS) Wu and Banzhaf ( 2010 ) 136 Co-Operation of Biology Related Algorithm (COBRA) Akhmedova and Semenkin ( 2013 ) 137 Coral Reefs Optimization (CRO) Salcedo-Sanz et al. ( 2014 ) 138 Corona Virus Optimization (CVO) Salehan and Deldari ( 2022 ) 139 Coronavirus Herd Immunity Optimizer (CHIO) Al-Betar et al. ( 2021 ) 140 Coronavirus Optimization Algorithm (COVIDOA) Khalid et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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“…The resulting CVO has been used to solve the combinatorial problem of finding the best selection of hyperparameters required for building a DL model in dealing with time series forecasting. In a related study 37 , a variant of CVO is being reported with emphasis on motivating the design of the algorithm based on the SIR model. Selected benchmark functions were used to evaluate the performance of the proposed version of the CVO, so that the problem of discrete and continuous mathematical functions is addressed.…”

Section: Related Workmentioning

confidence: 99%

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Oyelade

Ezugwu

2022

Sci Rep

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Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.

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“…The PSO mechanism has taken inspiration from bird flocking behavior simulation [ 22 ]. It is one of the Swarm Intelligence (SI) nature-inspired sub-domain under biological-based optimization algorithm along with ant colony optimization (ACO) [ 8 ], bacterial foraging optimization (BFO) [ 11 ], grey wolf optimization (GWO) [ 23 ], dragonfly algorithm (DA) [ 24 ], corona virus optimization (CVO) [ 25 ], and others. The PSO is categorized into a stochastic search strategy based on each particle's iterative interaction that forms the swarm.…”

Section: Background On Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain

Hamami

Ismail

2022

Arch Computat Methods Eng

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Swarm Intelligence (SI) is one of the research fields that has continuously attracted researcher attention in these last two decades. The flexibility and a well-known decentralized collective behavior of its algorithm make SI a suitable candidate to be implemented in the swarm robotics domain for real-world optimization problems such as target search tasks. Since the introduction of Particle Swarm Optimization (PSO) as a representation of the SI algorithm, it has been widely accepted and utilized especially in local and global search strategies. Because of its simplicity, effectiveness, and low computational cost, PSO has retained popularity notably in the swarm robotics domain, and many improvements have been proposed. Target search problems are one of the areas that have been continuously solved by PSO. This article set out to analyze and give the inside view of the existing literature on PSO strategies towards target search problems. Based on the procedure of PRISMA Statement review method, a systematic review identified 51 related research studies. After further analysis of these total 51 selected articles and consideration on the PSO components, target search components, and research field components, resulting in nine main elements related to the discussed topic. The elements are PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target available, target mobility status, experiment framework, and environment complexity. Several recommendations, opinions, and perfectives on the discussed topic are presented. Finally, recommendations for future research in this domain are represented to support future developments.

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Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic

Cited by 27 publications

References 86 publications

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain

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