Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

Abstract

Henry Gas Solubility Optimization Algorithm (HGSO) is a recently developed population-based metaheuristic algorithm in the literature. One notable feature of HGSO is that the algorithm divides its (single) population into a set of clusters that are individually mapped to an independent HGSO with its parameter settings (as well as its local best). At a glance, having multiple independent HGSO serving the given clusters in the population can definitely boost exploration (i.e., in terms of roaming the new potential region in the search space for better solution alternatives). However, a closer look reveals two main limitations. Firstly, HGSO-to-cluster mapping is statically defined. To be specific, the defined HGSO-to-cluster mapping does not consider its adaptive performance for the subsequent iteration. Secondly, HGSO implementation ignores the opportunity for hybridization with other meta-heuristic algorithms. With hybridization, one can compensate the limitation of a host algorithm with other algorithms' strength. Best results in the literature have often been associated with hybridization. Addressing these limitations, this paper proposes the development of Hybrid HGSO (HHGSO). Taking HGSO as the host algorithm, HHGSO is hybridized with four recently developed meta-heuristic algorithms, including Jaya Algorithm (JA), Sooty Tern Optimization Algorithm (STOA), Butterfly Optimization Algorithm (BOA) and Owl Search Algorithm (OSA). The individual mapping of each algorithm is made dynamic based on penalized and reward adaptive probability. Comparative performance of HHGSO with the aforementioned algorithms is conducted with a well-known Searchbased Software Engineering (SBSE) problem involving team formation problem. Additionally, the defined hybridization approach has also been adopted as a hybridization template for solving the combinatorial test generation problem with the same meta-heuristic algorithm combinations. Comparative performance is also undertaken against recently developed hyper-heuristic algorithms involving Exponential Monte Carlo with Counter, Modified Choice Function, Improvement Selection Rules, and Fuzzy Inference Selection. Our results indicate that the HHGSO hybridization has usefully improved the performance of the original HGSO and gives superior performance against the given competing algorithms.

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

“…Zamli et al [98] introduced an innovative version of the HGSO known as Hybrid HGSO (HHGSO). In contrast to its precursor, HHGSO incorporates the concept of multiple clusters, each employing distinct individual meta-heuristic algorithms with their parameters and local best.…”

A Decision Support System for Multiple Team Formation

“…Team Formation Problem consists of a set coverage problem [1] in which a group is selected from defined criteria to perform a task. It can be considered an NP-hard problem [1], [2], [3], in diverse applications such as academic collaborations [4], medical and sports teams [5] [6].…”

“…Figure6shows the People screen, where all professionals' cards are presented and viewed according to professional level(1) and availability status (2).…”