Binary multi-verse optimization algorithm for global optimization and discrete problems

Al-Madi, Nailah; Faris, Hossam; Mirjalili, Seyedali

doi:10.1007/s13042-019-00931-8

Cited by 66 publications

(44 citation statements)

References 36 publications

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“…Many recent studies have used this algorithm in order to solve various problems in different applications. Due to the wide range of applications and their needs, other variants of MVO such as Binary Multi-verse optimizer [41] and Multi-Objective Multi-Verse Optimizer (MOMVO) [42] have been presented. This metaheuristic algorithm inspired by famous theory called Multi-Verse theory.…”

Section: Multi-verse Optimizer (Mvo)mentioning

confidence: 99%

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Tabrizchi

2020

SN Appl. Sci.

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Breast cancer is among the most common cancers women got, which can be effectively cured providing that it is diagnosed at the early stages. In the current study, we attempted to classify breast cancer into two groups of malignant and benign by proposing a new ensemble learning method using Multi-Verse Optimizer (MVO) and Gradient Boosting Decision Tree (GBDT). Moreover, the prediction rate of GBDT has been shown to be desirable, its efficiency and classification accuracy are significantly dependent on feature selection and parameter setting. Based on the MVO, we attempted to propose an efficient approach to optimize feature selection and GBDT's parameters at the same time. In other words, the MVO algorithm is able to play the role of a tuner to set the GBDT's main parameters and optimize feature selection results. To implement and test the proposed approach, standard criteria (i.e. accuracy, sensitivity, specificity, etc.) was used for performance evaluation. Also, the datasets of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer were considered for this purpose. Comparing the results of GBDT-MVO model with other proposed models demonstrated that this model is more precise and has considerably lower variance in the case of a breast cancer diagnosis.

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Section: Multi-verse Optimizer (Mvo)mentioning

confidence: 99%

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Tabrizchi

2020

SN Appl. Sci.

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“…The work in [88] describes the Dragonfly algorithm, its variants, modifications, hybridizations in the following applied science areas: Machine Learning, Image Processing, Wireless, and Networking. * Reference [81] introduced a novel binary multi verse optimization algorithm. In the article, the authors compared the new algorithm to some other binary optimization algorithms, including binary DA.…”

Section: A Comparison Between Dragonfly Algorithm and Other Algomentioning

confidence: 99%

OUP accepted manuscript

2020

Journal of Computational Design and Engineering

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Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems.

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“…From the research point of view, these problems present interesting challenges in the areas of operations research, computational complexity, and algorithm theory. Examples of combinatorial problems are found in, scheduling problems [1,2], transport [2], machine learning [3], facility layout design [4], logistics [5], allocation resources [6,7], routing problems [8,9], robotics applications [10], civil engineering problem [11][12][13], engineering design problem [14], fault diagnosis of machinery [15], and social sustainability of infrastructure projects [16], among others. Combinatorial optimization algorithms should explore the solutions space to find optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

2020

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The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

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Binary multi-verse optimization algorithm for global optimization and discrete problems

Cited by 66 publications

References 36 publications

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

Breast cancer diagnosis using a multi-verse optimizer-based gradient boosting decision tree

OUP accepted manuscript

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

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