An enhanced grey wolf optimizer for numerical optimization

Sharma, Sakshi; Salgotra, Rohit; Singh, Urvinder

doi:10.1109/iciiecs.2017.8275908

Cited by 10 publications

(11 citation statements)

References 15 publications

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“…Gray wolves have a hierarchical and disciplined social life. They are classified into [39,40] 1. Alpha wolves are responsible for essential decision-making;…”

Section: Gray Wolf Optimizer Algorithmmentioning

confidence: 99%

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Seismic damage identification of moment frames based on random forest algorithm and enhanced gray wolf optimization

Nourizadeh

Seyedpoor

2023

Structural Design Tall Build

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The present study aims to identify damage in two-dimensional (2-D) moment frames using seismic responses by combining the random forest (RF) machine classifier and the enhanced gray wolf optimizer (EGWO) metaheuristic algorithm. First, a 2-D moment frame for the dynamic analysis is simulated using the finite element method (FEM). Then, the placement of sensors is optimized using a proposed optimal sensor placement (POSP) method, which is a combination of the iterated improved reduced system (IIRS) and the binary differential evolution (BDE) optimization algorithm. The acceleration responses of the moment frame having damaged elements under 1995 Kobe earthquake are measured at the optimal sensor placement. Then, the natural frequencies and mode shapes of the structure are extracted using the auto-regressive model with exogenous input method (ARX) as a system identification method. The natural frequencies are exploited to train an RF machine learning network that can find the damaged story of the moment frame. Then, EGWO is employed to accurately locate and quantify the damaged elements of the structure. The efficiency of the proposed method is assessed through considering a six-story frame with 18 elements, a seven-story frame with 49 elements, and the experimental data of an eightstory frame for various conditions. The results show that the RF algorithm has an outstanding performance to correctly find a damaged story. Furthermore, the location and severity of damaged elements are precisely determined by EGWO algorithm.As a final outcome, it is demonstrated that the two-step proposed method is very effective in seismically identifying damage to such structures.

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“…Gray wolves have a hierarchical and disciplined social life. They are classified into [39,40] 1. Alpha wolves are responsible for essential decision-making;…”

Section: Gray Wolf Optimizer Algorithmmentioning

confidence: 99%

“…Omega wolves follow the alpha, beta, and delta ones. The encircling behavior of gray wolves is mathematically formulated as [39,40] D ¼ jC:…”

Section: Mathematical Model Of the Gwo Algorithmmentioning

confidence: 99%

Seismic damage identification of moment frames based on random forest algorithm and enhanced gray wolf optimization

Nourizadeh

Seyedpoor

2023

Structural Design Tall Build

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“…GWO algorithm is deemed one of the recent algorithms in metaheuristic optimisation techniques and it is highly effective to transact with the optimisation problems. GWO algorithm simulates the pack lifestyle of the grey wolves actually in nature concerning hierarch of society and hunting techniques [31][32][33][34]. The selected types of grey wolves are α, β, δ, and ω.…”

Section: Gwo Algorithm Overviewmentioning

confidence: 99%

“…Consequently, many methods have been used to fine tune the PI controllers' parameters such Ziegler-Nichols method [13][14][15][16], symbiotic organisms search [17,18], many artificial intelligence methods (such as artificial neural network [19], parallel fuzzy controller [20], Takagi-Sugeno fuzzy system [21], and neuro-fuzzy system [22,23]) and adaptive searching mechanism or evolutionary computation algorithms (genetic algorithm (GA) [24][25][26][27], particle swarm optimisation [28][29][30], and grey wolf optimisation [31][32][33][34]). An evolutionary algorithm is representing a randomised search process that is inspired by natural behaviour and the social manner of species.…”

Section: Introductionmentioning

confidence: 99%

Hybrid cuckoo search algorithm and grey wolf optimiser‐based optimal control strategy for performance enhancement of HVDC‐based offshore wind farms

Mahmoud

Hasanien

Besheer

et al. 2020

IET Generation, Transmission & Distribution

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“…(2) The proposed algorithm is tested on 16 benchmark functions with a wide range of dimensions and varied complexities. (3) The performance of the proposed approach is compared with standard GWO, FWA, Moth Flame Optimization (MFO), Crow Search Algorithm (CSA) [30], improved Particle Swarm Optimization(IPSO) [31], Biogeography-based optimization (BBO), Particle Swarm Optimization (PSO), Enhanced GWO (EGWO) [32], and Augmented GWO (AGWO) [33].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Yue

Zhang

Xiao

2020

Sensors

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Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

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An enhanced grey wolf optimizer for numerical optimization

Cited by 10 publications

References 15 publications

Seismic damage identification of moment frames based on random forest algorithm and enhanced gray wolf optimization

Seismic damage identification of moment frames based on random forest algorithm and enhanced gray wolf optimization

Hybrid cuckoo search algorithm and grey wolf optimiser‐based optimal control strategy for performance enhancement of HVDC‐based offshore wind farms

A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

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