A multi-external archive-guided Henry Gas Solubility Optimization algorithm for solving multi-objective optimization problems. (March 2022)

• Record Type: Journal Article
• Authors: Kahloul, Soumaia; Zouache, Djaafar; Brahmi, Boualem; Got, Adel
• Is Part Of: Engineering applications of artificial intelligence. Volume 109(2022)
• Publication Date: 2022-03
• Subjects: Multi-objective problems -- Henry Gas Solubility Optimizer -- Pareto set -- External archive -- Crowding distance computation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104588
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper proposes a new multi-objective evolutionary algorithm by adapting the recent Henry Gas Solubility Optimization (HGSO) with multiple objectives. The proposed MOHGSO uses the Pareto dominance relation as means of comparison and integrates two types of archive, while an elite archive is used to store the Pareto solutions found over the evolutionary process, the other external-archives are used to store the local best solutions corresponding to each cluster. Moreover, efficient archiving and leader selection strategies based on the crowding distance computation are proposed to guide the population towards the true Pareto front. The performance of the MOHGSO algorithm is validated through an extensive comparison with three well-known algorithms on twelve test functions and four engineering design problems. The experiments of two widely used metrics in the field called IGD and Sp metrics show the ability of the proposed algorithm in achieving interesting results. Furthermore, the statistical results related to the Wilcoxon test indicate that the proposed algorithm outperforms significantly the selected methods for the above metrics at a 99% confidence level.