What question did this study set out to answer?

The aim is to enhance clustering techniques by integrating evolutionary methods and local search strategies.

June 19, 2026Open Access

Improving Approach of Evolutionary Strategies for Clustering Technique Enhancement

DSDuaa Mahde SalehIraqi University HAHasanen S. AbdullahUniversity of Technology - Iraq AZAhmad ZamsuriUniversitas Lancang Kuning

Key Points

The aim is to enhance clustering techniques by integrating evolutionary methods and local search strategies.
Adopts an advanced evolutionary approach combining Differential Evolution and Genetic Algorithm with local search techniques.
Validates the approach on datasets including UTK face, Academic Group news, and weather.
Implements methods to balance exploration and exploitation in clustering processes.
Achieves silhouette scores greater than 0.90 across all test scenarios.
Demonstrates increased efficiency and flexibility compared to baseline metaheuristic methods.
Shows significant improvement in clustering performance.

Abstract

The existence of the information has been the essential aspect of the whole society. Information is concentrated in all forms to be effectively utilized. Clustering — an unsupervised learning technique. It is based on data similarity that gives rise to issues in collection, challenges and instability in data structure. It proposes an advanced evolutionary method by combining two approaches. Firstly, it adopts the evolutionary approach and integrates the advantages between two methods to design one. Among them are Differential Evolution (DE) and Genetic Algorithm (GA), Evolutionary Strategy (ES) and Genetic Programming (GP), and Evolutionary Programming (EP) and Particle Swarm Optimization (PSO). Second, it contains local search in order to improve the exploitation and to ensure the balance between exploration and exploitation within the collection (DE algorithm, ES, and EP). The enhanced approach is validated on three datasets: UTK face, Academic Group (AG) news, and weather. The next results show that the proposed approach is more efficient and flexible. It consistently obtains silhouette scores greater than 0.90 in every test scenario. This is a significant improvement over baseline metaheuristic clustering methods.

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