Grasshopper Optimisation Algorithm: Theory and application

Saremi, Shahrzad; Mirjalili, Seyedali; Lewis, Andrew

doi:10.1016/j.advengsoft.2017.01.004

Cited by 2,043 publications

(1,023 citation statements)

References 59 publications

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“…, as noted in [3], linearly decreases the space that the grasshoppers should explore and exploit. Finally function s defines the strength of social forces, and was established by creators of the algorithm as:…”

Section: B Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…Grasshopper Optimization Algorithm claims to be inspired by the social behavior of grasshoppers -insects of Orthoptera order (suborder Caelifera) [3]. Each member of the swarm constitutes a single insect located in search space S and moving within its bounds.…”

Section: B Grasshopper Optimization Algorithmmentioning

confidence: 99%

“…Grasshopper Optimization Algorithm (GOA) is an optimization technique introduced by Saremi, Mirjalili and Lewis in 2017 [3]. It includes both social interaction between ordinary agents (grasshoppers) and the attraction of the best individual.…”

mentioning

confidence: 99%

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Data Clustering with Grasshopper Optimization Algorithm

Łukasik¹,

Kowalski²,

Charytanowicz³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem of data analysis. It can be solved with broad range of methods employing statistical approaches or heuristic procedures. The latter often include mechanisms known from nature as they are known to serve as useful components of effective optimizers. The paper investigates the possibility of using novel nature-inspired technique -Grasshopper Optimization Algorithm (GOA) -to generate accurate data clusterings. As a quality measure of produced solutions internal clustering validation measure of Calinski-Harabasz index is being employed. This paper provides description of proposed algorithm along with its experimental evaluation for a set of benchmark instances. Over a course of our study it was established that clustering based on GOA is characterized by high accuracy -when compared with standard K-means procedure.

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Section: B Grasshopper Optimization Algorithmmentioning

confidence: 99%

Section: B Grasshopper Optimization Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Data Clustering with Grasshopper Optimization Algorithm

Łukasik¹,

Kowalski²,

Charytanowicz³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Grasshopper Optimisation Algorithm (GOA) [16] is a recently proposed naturally inspired algorithm, which is based on one of the largest swarms of all creatures. As grasshoppers are herbivores, they cause severe damage to crops.…”

Section: Grasshopper Optimisation Algorithm: An Overviewmentioning

confidence: 99%

“…CSA mimics the behavior of crow to store their excess food in hiding places and retrieve it when it is needed. Similarly, Ant Lion Optimizer Algorithm [15] and Grasshopper Optimisation Algorithm [16] are also a good example of social mimicry of the natural swarms.…”

Section: Introductionmentioning

confidence: 99%

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

Saxena

Shekhawat

Kumar

2018

Modelling and Simulation in Engineering

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In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. e ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. ese two phases are tied with a bridging mechanism, which plays an important role. is paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. is experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). e performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.

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A novel fuzzy tuned multistage PID approach for frequency dynamics control in an islanded microgrid

Annamraju

Nandiraju

2020

Int Trans Electr Energ Syst

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Objective: In microgrid (MG), besides the load perturbation, volatile nature in renewable output power along with energy storage system and inertia uncertainties cause large frequency deviations which may weaken the MG and could lead to complete blackout. Therefore, MG requires an intelligent, efficient and robust control method. Methods: In response to this challenge, this paper proposes a novel fuzzy logic approach (FLA) tuned multistage PID controller for frequency control of an islanded MG in the presence of high renewable penetration. The proposed controller is intended for principal parameter tuning of multistage PID controller under critical operating conditions as mentioned in the objective. Results: The real-time MG test system is considered to present the simulation results. The test system is modelled in Matlab/Simulink. The results are carried out under different operating scenarios and the performances of the proposed technique are compared with various existing techniques in the literature. Conclusions: The simulation outcomes divulge that the proposed controller is more superior in improving the frequency dynamics of the MG (in terms of settling time, overshoot and error reduction) under various disturbance conditions. Furthermore, the proposed controller is more robust to renewable and energy storage system uncertainties in MG and less sensitive to MG parametric variations as compared to other controllers in literature.

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Grasshopper Optimisation Algorithm: Theory and application

Cited by 2,043 publications

References 59 publications

Data Clustering with Grasshopper Optimization Algorithm

Data Clustering with Grasshopper Optimization Algorithm

Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms

A novel fuzzy tuned multistage PID approach for frequency dynamics control in an islanded microgrid

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