Omkar Kulkarni

Abstract: The Cuckoo Search algorithm is a recently developed meta-heuristic optimization algorithm, which is used for solving optimization problems. This is a nature-inspired metaheuristic algorithm, which is based on the brood parasitism of some cuckoo species, along with Levy flights random walks. Normally, the parameters of the cuckoo search are kept constant for certain duration, this results into decrease the efficiency of the algorithm. To make a deal with this issue, a proper strategy for tuning the cuckoo search parameters is to be defined. Cuckoos are fascinating birds, not only because of the beautiful sounds they can make but also because of their aggressive reproduction strategy. Some species such as the Ani and Guira cuckoos lay their eggs in host bird nest, and they may remove others eggs to increase the hatching probability of their own. In this paper, cuckoos behaviour & their egg laying strategy in the nests of other host birds is explained.

Abstract: Genetic Algorithm is optimization method based on the mechanics of natural genetics and natural selection. Genetic Algorithm mimics the principle of natural genetics and natural selection to constitute search and optimization procedures.GA is used for scheduling to find the near to optimum solution in short time. In a genetic algorithm representation is done with variable length of sub-chromosome.GA is developed to generate the optimal order scheduling solution. GA is used as tool in different processes to optimize the process parameters. This paper reviews the genetic algorithms that are designed for solving multiple problems in applications of material science and manufacturing in field of mechanical engineering. Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.

Abstract: Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behaviour of bird flocking or fish schooling. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and lbest locations (local version of PSO). In past several years, PSO has been successfully applied in many research and application areas. This paper reviews the applications of PSO algorithm in mechanical domain. The applications of PSO include optimal weight design of a gear train, Simultaneous Optimization of Design and Machining Tolerances, Process Parameter Optimization in Casting, and Machine Scheduling Problem. The paper also describes the improved version of PSO algorithm namely: Hybrid PSO, Multiobjective PSO, Adaptive PSO and Discrete PSO.

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.

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.

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.

Abstract: In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60%, (2) potassium hydroxide catalyst concentration: 0.84%(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19%. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42%. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.

Abstract: Accurate parameter identification is crucial for a precise PV cell modelling and analysis of characteristics of PV systems, while high nonlinearity of output I-V curve makes this problem extremely thorny. Hence, a large number of researches have aroused extensive interests in the past few years. Due to the rapid advancement of computer technology and swarm intelligence, various promising meta-heuristic algorithms have been proposed to further accelerate this trend. This paper aims to undertake a comprehensive review on meta-heuristic algorithms and related variants which have been applied on PV cell parameter identification. Particularly, these algorithms are classified into four categories, e.g., biology-based algorithms, physics-based algorithms, sociology-based algorithms and mathematics-based algorithms. Meanwhile, the evaluation criteria and identification performance of each algorithm are thoroughly addressed. Besides, in order to quantitatively evaluate and compare various algorithms, the identified PV parameters including the specific error and the simulated output I-V or P-V curves are provided at the end of each algorithm. Moreover, a comprehensive summary is also introduced to more specifically guide the readers to grasp and utilize these approaches. Lastly, based on the covered twenty-eight algorithms, conclusion presents some perspectives and recommendations for future development.

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.

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.