Collaborative Strategy for Grey Wolf Optimization Algorithm

Alzaghoul, Esra; Fakhouri, Sandi N.

doi:10.5539/mas.v12n7p73

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 1 demonstrates the GWO research trends in the past five years. According to literature [41], [49], the GWO research can be divided into following four categories:…”

Section: Literature Reviewmentioning

confidence: 99%

Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

et al. 2020

View full text Add to dashboard Cite

Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski's information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided. INDEX TERMS Population-based search approaches, evolutionary computation, unconstrained optimization, grey wolf optimization, global search, Minkowski's formula. The associate editor coordinating the review of this manuscript and approving it for publication was Huaqing Li.

show abstract

“…Figure 1 demonstrates the GWO research trends in the past five years. According to literature [41], [49], the GWO research can be divided into following four categories:…”

Section: Literature Reviewmentioning

confidence: 99%

Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Metaheuristic algorithms are used to solve complex optimization problems in a wide range of fields including engineering, economics, information technology, etc. [1]. Many metaheuristic algorithms have been proposed: Artificial Bee Colony [8], Grasshopper Optimization Algorithm [17], Genetic Algorithm [2], Simulated Annealing Algorithm [2], Lion Optimization Algorithm [23], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective global optimization of grillage-type engineering structures using advanced metaheuristics

Belevičius

Mačiūnas

Šešok

2020

ACUTM

View full text Add to dashboard Cite

The purpose of the paper is to present the method implemented for a global optimization of grillage-type pile foundations introducing two advanced metaheuristics: AAGA and AGADS. The suggested new optimization algorithm including the synergy of AAGA and AGADS demonstrates improved results comparing with former AGA and GADS. Compromise objective function to be minimized involves the maximum reactive force in piles and maximum bending moment in the connecting beams. The feasibility of a simple weighting technique for the objective function is proved by numerical investigation of objective function domain for several different topologies of foundations. Sizing problem of connecting beams is solved together with the optimization problem. The original finite element program was employed for solution of direct problem.

show abstract

Collaborative Strategy for Grey Wolf Optimization Algorithm

Cited by 2 publications

References 25 publications

Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization

Multi-objective global optimization of grillage-type engineering structures using advanced metaheuristics

Contact Info

Product

Resources

About