An external archive guided Harris Hawks optimization using strengthened dominance relation for multi-objective optimization problems

Zouache, Djaafar; Got, Adel; Drias, Habiba

doi:10.1007/s10462-022-10235-z

Cited by 7 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi objective version of HHO (MOHHO) is proposed in Zouache et al (2023). MOHHO uses the strengthened dominance relation to select the solutions with better convergence and diversity balance.…”

Section: Harris Hawks Optimization (Hho)mentioning

confidence: 99%

Enhanced migrating birds optimization algorithm for optimization problems in different domains

Algin,

Alkaya,

Agaoglu

2024

Ann Oper Res

View full text Add to dashboard Cite

Migrating birds optimization algorithm is a promising metaheuristic algorithm recently introduced to the optimization community. In this study, we propose a superior version of the migrating birds optimization algorithm by hybridizing it with the simulated annealing algorithm which is one of the most popular metaheuristics. The new algorithm, called MBOx, is compared with the original migrating birds optimization and four well-known metaheuristics, including the simulated annealing, differential evolution, genetic algorithm and recently proposed harris hawks optimization algorithm. The extensive experiments are conducted on problem instances from both discrete and continuous domains; feature selection problem, obstacle neutralization problem, quadratic assignment problem and continuous functions. On problems from discrete domain, MBOx outperforms the original MBO and others by up to 20.99%. On the continuous functions, it is observed that MBOx does not lead the competition but takes the second position. As a result, MBOx provides a significant performance improvement and therefore, it is a promising solver for computational optimization problems.

show abstract

Section: Harris Hawks Optimization (Hho)mentioning

confidence: 99%

Enhanced migrating birds optimization algorithm for optimization problems in different domains

Algin,

Alkaya,

Agaoglu

2024

Ann Oper Res

View full text Add to dashboard Cite

show abstract

“…

is the decision vector [20] .

is the set of feasible solutions, which is formed by the intersection of the constraints of the optimization problem [21] . We denote the image of the decision space by

and we call it an objective space [22] .…”

Section: Preliminariesmentioning

confidence: 99%

A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching and relocation problem during COVID-19

Hemici

Zouache

Brahmi

et al. 2023

Applied Soft Computing

View full text Add to dashboard Cite

“…We presume that they will descend into a space of D dimensions, guided by the function of levy flight (LF) [32,33], by adhering to the following formula:…”

Section: Exploitation Phasementioning

confidence: 99%

“…The second investigation merges strategies of Epsilon-Dominance and crowding computation to tackle MOPs, and incorporates an external limited-size repository into the HHO algorithm to uphold the concept of elitism [28]. In the final study, a combination of strengthened dominance and a population archive is utilized to preserve the set of optimal solutions throughout the optimization process [29]. These innovative approaches collectively contribute to the ongoing evolution of the HHO algorithm for multi-objective optimization.…”

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

View full text Add to dashboard Cite

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.

show abstract

An external archive guided Harris Hawks optimization using strengthened dominance relation for multi-objective optimization problems

Cited by 7 publications

References 45 publications

Enhanced migrating birds optimization algorithm for optimization problems in different domains

Enhanced migrating birds optimization algorithm for optimization problems in different domains

A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching and relocation problem during COVID-19

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

Contact Info

Product

Resources

About