English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper is concerned with data clustering to separate clusters based onthe connectivity principle for categorizing similar and dissimilar data intodifferent groups. Although classical clustering algorithms such as K-means areefficient techniques, they often trap in local optima and have a slowconvergence rate in solving high-dimensional problems. To address these issues,many successful meta-heuristic optimization algorithms and intelligence-basedmethods have been introduced to attain the optimal solution in a reasonabletime. They are designed to escape from a local optimum problem by allowingflexible movements or random behaviors. In this study, we attempt toconceptualize a powerful approach using the three main components: ChimpOptimization Algorithm (ChOA), Generalized Normal Distribution Algorithm(GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions ofChOA with two different independent groups' strategies and seven chaotic maps,entitled ChOA(I) and ChOA(II), are presented to achieve the best possibleresult for data clustering purposes. Secondly, a novel combination of ChOA andGNDA algorithms with the OBL strategy is devised to solve the majorshortcomings of the original algorithms. Lastly, the proposed ChOAGNDA methodis a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can beused to tackle large and complex real-world optimization problems, particularlydata clustering applications. The results are evaluated against seven popularmeta-heuristic optimization algorithms and eight recent state-of-the-artclustering techniques. Experimental results illustrate that the proposed worksignificantly outperforms other existing methods in terms of the achievement inminimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowestError Rate (ER), accelerating the convergence speed, and finding the optimalcluster centers.

iran journal of computer science

A Hybrid Chimp Optimization Algorithm and Generalized Normal  Distribution Algorithm with Opposition-Based Learning Strategy for Solving  Data Clustering Problems