Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems

TL;DR

This paper introduces a hybrid HGSO algorithm with dynamic cluster-to-algorithm mapping, enabling multiple meta-heuristics to coexist and adaptively switch, leading to improved performance in search-based software engineering problems.

Contribution

It presents a novel hybrid HGSO with dynamic clustering and adaptive switching, integrating multiple meta-heuristics for enhanced problem-solving capabilities.

Findings

Significant performance improvement over traditional HGSO.

Outperforms other meta-heuristic algorithms in case studies.

Effective dynamic cluster-to-algorithm mapping demonstrated.

Abstract

This paper discusses a new variant of the Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO allows multiple clusters serving different individual meta-heuristic algorithms (i.e., with its own defined parameters and local best) to coexist within the same population. Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization consisting of Jaya Algorithm, Sooty Tern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl Search Algorithm, respectively. The acquired results from the selected two case studies (i.e., involving team formation problem and combinatorial test suite generation) indicate that the hybridization has notably improved the performance of HGSO and gives superior performance against other competing meta-heuristic and hyper-heuristic algorithms.