Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Neve, Abhishek G; Kakandikar, Ganesh M.; Kulkarni, Omkar

doi:10.4172/2090-4908.1000165

Cited by 29 publications

(19 citation statements)

References 14 publications

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“…It has been reported that GOA is capable to solve almost all types of problems efficiently. In [47] , [48] , [49] , [50] , the authors applied GOA to solve various problems. The results demonstrate the superiority of GOA in comparison with the tasted algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In the present research, GOA has been applied to optimally place multiple ONUs in the FiWi access network. In [23] , [47] , [48] , [49] , [50] , GOA has been used for solving several problems in engineering and application. Further in [23] , the authors proposed GOA and accomplished the performance analysis of GOA.…”

Section: Related Literaturementioning

confidence: 99%

“…GOA has provided satisfactory solutions and has the potential to significantly outperform several reported algorithms. The authors implemented GOA in multi-objective problems [47] , in unconstrained and constrained test functions [48] , in Economic Load Dispatch (ELD) [49] , in satellite image segmentation [50] etc. These works report the efficacy of GOA in comparison with the reported optimizers.…”

Section: Related Literaturementioning

confidence: 99%

See 2 more Smart Citations

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

Singh

Prakash

2020

Optical Fiber Technology

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Section: Resultsmentioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

Singh

Prakash

2020

Optical Fiber Technology

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“…In which, the number of grasshoppers involved in the population is indicated as N . A good metaheuristic algorithm should determine the optimal solution with better balanced exploitation and exploration process 37 . During different exploration and exploitation phases, the mathematical model achieved is expressed as follows:

X_{i}^{d} = c ((), \sum_{j = 1, j \neq i}^{N} c \frac{U_{d} - L_{d}}{2} s ((), |x_{j}^{d} - x_{i}^{d}|) \frac{x_{j} - x_{i}}{d_{italicij}}) + T_{d}

where the upper bound and lower bound variables in d th dimension is represented as U d and L d .…”

Section: Proposed Approachmentioning

confidence: 99%

An adaptive technique based blood glucose control in type‐1 diabetes mellitus patients

Belmon¹,

Auxillia

2020

Numer Methods Biomed Eng

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This study proposes Grasshopper Optimization Algorithm (GOA) based type 1 diabetes mellitus system utilizing the nonlinear Bergman minimal model with proportional integral derivative (PID) controller. GOA is the optimization algorithm, which is utilized for selecting the optimized tuning parameters of the PID controller also solves the nonlinear system parameter identification problem. The novelty of the proposed study is to stabilize the glucose level in blood for type 1 diabetic patients by infusion of insulin in reduced time with optimal quantity. Without any intervention to the normal activities of patients, the supply of insulin injection and glucose monitoring is performed automatically for type 1 diabetic patients using this controller. In between the measured variable and set point, the difference is calculated by the PID controller to evaluate an error values. In realistic patient oriented conditions, the control performance evaluation, control optimization, and advanced patient modelling should be highly concentrated during the research/analysis on blood glucose control. Evaluation is performed to analyze control performances and implementation is done on Simulink/MATLAB environment. The performance analysis of the type 1 diabetes mellitus system with GOA technique is also discussed and to improve the control performance, to optimize the controller parameters. The simulation results have proved the substantial improvement in the performance of proposed algorithm with the better results achieved than the other conventional controllers such as PSO‐PID and EHO‐PID.

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“…The SVM-CBO approach has been validated on three well-known 2D test functions for CGO, that are: Rosenbrock constrained to a disk [55], Rosenbrock constrained to a line and a cubic [55,56], and Mishra's Bird constrained [57]. Since these test functions are all constrained to just one connected feasible region, two more supplementary test functions have been defined: Branin (rescaled) [58] constrained to an ellipse and Branin (rescaled) constrained on two disconnected ellipses.…”

Section: Simple 2d Test Functionsmentioning

confidence: 99%

Optimizing Partially Defined Black-Box Functions Under Unknown Constraints via Sequential Model Based Optimization: An Application to Pump Scheduling Optimization in Water Distribution Networks

Candelieri

Galuzzi

Giordani

et al. 2020

Lecture Notes in Computer Science

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This paper presents a sequential model based optimization framework for optimizing a black-box, multi-extremal and expensive objective function, which is also partially defined, that is it is undefined outside the feasible region. Furthermore, the constraints defining the feasible region within the search space are unknown. The approach proposed in this paper, namely SVM-CBO, is organized in two consecutive phases, the first uses a Support Vector Machine classifier to approximate the boundary of the unknown feasible region, the second uses Bayesian Optimization to find a globally optimal solution within the feasible region. In the first phase the next point to evaluate is chosen by dealing with the trade-off between improving the current estimate of the feasible region and discovering possible disconnected feasible sub-regions. In the second phase, the next point to evaluate is selected as the minimizer of the Lower Confidence Bound acquisition function but constrained to the current estimate of the feasible region. The main of the paper is a comparison with a Bayesian Optimization process which uses a fixed penalty value for infeasible function evaluations, under a limited budget (i.e., maximum number of function evaluations). Results are related to five 2D test functions from literature and 80 test functions, with increasing dimensionality and complexity, generated through the Emmental-type GKLS software. SVM-CBO proved to be significantly more effective as well as computationally efficient.

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Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Cited by 29 publications

References 14 publications

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

An adaptive technique based blood glucose control in type‐1 diabetes mellitus patients

Optimizing Partially Defined Black-Box Functions Under Unknown Constraints via Sequential Model Based Optimization: An Application to Pump Scheduling Optimization in Water Distribution Networks

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