Journal ArticleDOI
Automatic clustering using quantum-based multi-objective emperor penguin optimizer and its applications to image segmentation
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TLDR
The two new operations namely weighted cluster centroid computation and threshold setting are introduced to refine cluster centroids and a novel fitness function strategy is also proposed for efficient searching.Abstract:
In this study, a novel quantum-based multi-objective is proposed using Schrodinger equations. The two new operations namely weighted cluster centroid computation and threshold setting are also intr...read more
Citations
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MOEPO: A novel Multi-objective Emperor Penguin Optimizer for global optimization: Special application in ranking of cloud service providers
TL;DR: Improvements in tackling the resource scheduling issue in cloud computing have been established by using the proposed algorithm, which has the feature to cache the non-dominated Pareto optimal solutions.
Journal ArticleDOI
A Comprehensive Review on Multi-objective Optimization Techniques: Past, Present and Future
Shubhkirti Sharma,Vijay Kumar +1 more
Journal ArticleDOI
Optimization of residential building envelopes using an improved Emperor Penguin Optimizer
TL;DR: Among different forms that have been evaluated in this paper, the trapezoid and the rectangle forms had better results with the fewest amount of the life cycle costs in all five various climate conditions.
Journal ArticleDOI
A systematic review on emperor penguin optimizer
TL;DR: In this article, the authors present an in-depth study related to the current EPO's adoption in the scientific literature and highlight new potential areas for improvements (and omission), the finding of this study can serve as guidelines for researchers and practitioners to improve the current state-of-the-art and stateof-practices on general adoption of EPO while highlighting its new emerging areas of applications.
Journal ArticleDOI
Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms
TL;DR: A critical review of meta-heuristic algorithms for future reference, including concepts, applications, advantages and disadvantages, before focusing on one specific meta heuristic algorithm, namely, Emperor Penguin Optimizer (EPO), is presented in this article .
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Journal ArticleDOI
Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Journal ArticleDOI
Automatic Clustering Using an Improved Differential Evolution Algorithm
TL;DR: Differential evolution has emerged as one of the fast, robust, and efficient global search heuristics of current interest as mentioned in this paper, which has been applied to the automatic clustering of large unlabeled data sets.
Journal ArticleDOI
Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications
Gaurav Dhiman,Vijay Kumar +1 more
TL;DR: The main concept behind this algorithm is the social relationship between spotted hyenas and their collaborative behavior and it is revealed that the proposed algorithm performs better than the other competitive metaheuristic algorithms.
Journal ArticleDOI
An Evolutionary Approach to Multiobjective Clustering
Julia Handl,Joshua Knowles +1 more
TL;DR: The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature and an evolutionary approach to the problem is developed.
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