Omkar Kulkarni

Bio: Omkar Kulkarni is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Metaheuristic & Penalty method. The author has an hindex of 6, co-authored 8 publications receiving 240 citations.

Constrained cohort intelligence using static and dynamic penalty function approach for mechanical components design

Abstract: Most of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems.

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.

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.