Abhishek G. Neve

Bio: Abhishek G. Neve is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Algorithm design & Engineering optimization. The author has an hindex of 2, co-authored 2 publications receiving 29 citations.

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Abstract: Grasshopper Optimization algorithm is one of the recent algorithm for optimization. This algorithm is swarm based nature inspired algorithm which mimics and mathematically models the behaviour of grasshopper swarm in nature. The proposed algorithm can be used for solving the engineering optimization problems. The GOA is tested for different benchmark test functions to validate and verify the performance of the algorithm. Results obtained from GOA are compared with actual values (results) of the test functions. The results obtained from algorithm show that the algorithm is able to give the accurate results. The unconstrained and constrained test functions solved by using the Grasshopper optimization Algorithm (GOA) and the results can validate that the algorithm gives the trustable results. Constraints handling technique is used to convert the constrained optimization problem into unconstrained optimization problem, so that the problem can be handled by the Grasshopper Optimization Algorithm (GOA). Static penalty method is used as a constraints handling technique in this paper. The algorithm can also apply for different engineering problems in real life.

Optimization of Railway Bogie Snubber Spring with Grasshopper Algorithm

Abstract: Swarm intelligence is a branch which deals in research that models the population of interacting agents or swarms that are self-organizing in nature. Grasshopper optimization algorithm is a modern algorithm for optimization which is inspired from the swarm-based nature. This algorithm simulates the behaviour of the grasshopper in nature and models that mathematically for solving optimization problems. Grasshopper optimization algorithm is used for the optimization of mechanical components and systems. Snubber spring is a kind of helical spring which is a part of suspension system in railway bogie. In this work, the design of snubber spring is optimized by using grasshopper optimization algorithm. The suspension system of railway bogie consists of inner spring, outer spring, and snubber spring. Optimization is done for the weight minimization of snubber spring. Wire diameter, number of active turns and mean coil diameter are the design parameters for the optimization. These parameters are optimized by using grasshopper optimization algorithm according to bounds, loading, and boundary conditions. The optimized parameters are validated experimentally and also by using a software. The spring is modelled in CATIA V5 and analyzed in ANSYS 17.0. The comparison of results is done and is validated with results experimentally in which the spring is tested on universal testing machine for compression test.

Hybrid optimization with cryptography encryption for medical image security in Internet of Things

Abstract: The development of the Internet of Things (IoT) is predicted to change the healthcare industry and might lead to the rise of the Internet of Medical Things. The IoT revolution is surpassing the present-day human services with promising mechanical, financial, and social prospects. This paper investigated the security of medical images in IoT by utilizing an innovative cryptographic model with optimization strategies. For the most part, the patient data are stored as a cloud server in the hospital due to which the security is vital. So another framework is required for the secure transmission and effective storage of medical images interleaved with patient information. For increasing the security level of encryption and decryption process, the optimal key will be chosen using hybrid swarm optimization, i.e., grasshopper optimization and particle swarm optimization in elliptic curve cryptography. In view of this method, the medical images are secured in IoT framework. From this execution, the results are compared and contrasted, whereas a diverse encryption algorithm with its optimization methods from the literature is identified with the most extreme peak signal-to-noise ratio values, i.e., 59.45 dB and structural similarity index as 1.

A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

Abstract: The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

Expeditious frequency control of solar photovoltaic/biogas/biodiesel generator based isolated renewable microgrid using grasshopper optimisation algorithm

Abstract: This work endeavours to propose an approximate mathematical model of a community based renewable microgrid with solar photovoltaic, biogas and biodiesel generators including battery storage for load frequency studies. It becomes a great challenge to coordinate between generation and load demand of the microgrid as the renewable sources are highly unpredictable and nature dependent. To overcome this issue, the responses of the system are studied under different real-world scenarios of renewable source availabilities and load variations with a maiden approach towards optimising the controller gains using a recent grasshopper optimisation algorithm (GOA) for efficient frequency control. The frequency responses of proposed microgrid are compared with different conventional controllers and some popular optimisation algorithms using MATLAB/Simulink. Finally, proportional-integral-derivative controller with GOA is preferred for the case studies under four cases of source variations with step load perturbation and one case of simultaneous source and load variations. The results of all these five scenarios are found satisfactory in terms of frequency responses and reported in the work.

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Abstract: Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.