Multi-population Black Hole Algorithm for the problem of data clustering

Abstract

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization

“…Feature selection, a challenging problem, is addressed using the Grasshopper Optimization Algorithm (GOA) with Evolutionary Population Dynamics to mitigate convergence and stagnation drawbacks, revealing superior performance on various datasets [ 34 ]. the Black Hole Algorithm is introduced as a nature-inspired optimization algorithm for data clustering, and a multi-population version is proposed, exhibiting precise results and high convergence rates on benchmark functions and real datasets [ 35 ].…”

Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

“…As optimization problems continue to evolve, new techniques encompassing artificial intelligence, as well as the metaheuristic search-based optimization approaches were designed to tackle the D-OPF problem. Recent efforts focused on search-based optimization approaches, which include the genetic algorithm (GA) optimization method [10], particle swarm optimizer (PSO) method [11,12], differential evolution optimization method [13,14], enhanced genetic algorithms optimization method [15], gravitational searching algorithm (GSA) method [16,17], multi-phase searching optimization algorithm [18,19], improving colliding bodies method [20], improved PSO method [21], biogeography-based optimizing approach [22], fuzzy-based hybrid PSO method [23], blackhole optimization approach [24], imperialist competitive optimization algorithm [25], harmony search optimization algorithm [26], PSO hybrid with GSA method [27], grey wolf optimization technique [28], and bee colony optimization approach [29]. Additionally, many multi-objective functions have been introduced for the D-OPF in [30,31].…”