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

arXiv: https://arxiv.org/abs/2105.14923 · HTML: https://mlsanity.com/paper/2105.14923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 77 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 6 of 6)

- [Flower Pollination Algorithm for Global Optimization](https://mlsanity.com/paper/1312.5673/) (2013)
- [An Evaluation of Monte Carlo-Based Hyper-Heuristic for Interaction Testing of Industrial Embedded Software Applications](https://mlsanity.com/paper/2002.07443/) (2020)
- [Supplementary Material for the Information Sciences Paper: An Experimental Study of Hyper-Heuristic Selection and Acceptance Mechanism for Combinatorial t-way Test Suite Generation](https://mlsanity.com/paper/1702.04501/) (2017)
- [Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms](https://mlsanity.com/paper/1902.08001/) (2019)
- [A Hybrid Q-Learning Sine-Cosine-based Strategy for Addressing the Combinatorial Test Suite Minimization Problem](https://mlsanity.com/paper/1805.00873/) (2018)
- [Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites](https://mlsanity.com/paper/1906.08855/) (2019)

## Abstract (arXiv)

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.