Multi-objective Harris Hawks Optimization for Optimal Emergency Vehicle Dispatching During a Pandemic

Khennak, Ilyes; Drias, Habiba; Khelfa, Celia; Drias, Yassine; Bourouhou, Nour el Houda; Zafoune, Imene

doi:10.1007/978-3-031-27524-1_83

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Within the existing body of literature, three notable methods have been proposed that aim to extend the conventional HHO algorithm specifically for addressing MOPs. The first of these incorporates an archive to sustain non-dominated solutions and employs the roulette wheel technique as a selection strategy [27]. However, it is noteworthy that the qualitative outcomes of this version are validated using only the IGD metric, with no consideration for diversity.…”

Section: Introductionmentioning

confidence: 99%

An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization

Boumaza,

Benyamina,

Zouache

et al. 2023

ISI

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The Harris Hawks Optimizer (HHO) is a bio-inspired metaheuristic acknowledged for its effectiveness in addressing mono-objective optimization problems. However, its application has been limited to these specific challenges. To overcome this constraint and to navigate complex multi-objective optimization challenges, a Guided Multi-Objective variant of HHO, termed as Guided Multi-Objective Harris Hawks Optimization (GMOHHO), is introduced in this study. In the developed GMOHHO algorithm, an archival mechanism is integrated. This mechanism is specifically designed to store non-dominated solutions and to enhance their retrievability during the search process. Moreover, a robust multi-leader selection procedure is implemented, facilitating the steering of the primary set of solutions towards potential areas within the search space. Further, the Bi-Goal Evolution (BIGE) framework is utilized. This framework aids in the transformation of a search space with multitudinous objectives into a bi-objective one, thereby augmenting environmental selection. This integration ensures a balanced compromise between the convergence and diversity of solutions. The performance of the proposed GMOHHO algorithm was appraised across a series of test functions. The results consistently displayed its supremacy over the conventional HHO approach as well as other cutting-edge multi-objective optimization techniques. With its noteworthy capability to address a broad range of multiobjective optimization problems, the GMOHHO algorithm delivers high-quality solutions within acceptable computational timeframes. This study, therefore, paves the way for a promising approach to multi-objective optimization, potentially expanding the application sphere of the HHO algorithm.

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Section: Introductionmentioning

confidence: 99%