Cloud environment provides a shared pool of resources to various users all around the world. The cloud model has the physical machines and the virtual machines for processing the tasks from the users in a parallel manner. In certain situations, the user’s demand may be high, which leads to the overloading of the processing units, and this situation affects the performance of the cloud setup. Several works have introduced the load balancing strategy to balance the load of the cloud environment, but they lack in the ability to reduce the number of task migrations. This paper introduces the load balancing strategy by defining the optimization algorithm and the multi-objective model. This research introduces the Crow search with the integrated Fractional Dragonfly Algorithm (C-FDLA), for load balancing through the hybridization of the Crow Search Algorithm (CSA), Dragonfly Algorithm (DA) and the fractional calculus. Also, the paper uses the multi-objective model based on selection probabilities, the frequency scaling based capacity of the machine and the data length of the task. The performance of the proposed C-FDLA is analyzed under different cloud scenarios, and from the results, it is evident that the proposed work has achieved significant performance with the minimum load of 0.0913 and number of tasks reallocated as 11.

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This manuscript discusses about the Parameter estimation of Induction motor by utilizing the soft computing methodologies that is by using evolutionary algorithms such as Genetic algorithm, Particle swarm optimization, Artificial immune algorithm to overcome the difficulties in the conventional method where we calculating the per phase equivalent circuit parameters from the No load test and Blocked rotor test which compromises in result in terms of accuracy of the result and also evaluated the accuracy of the different algorithm in estimating the parameters of the induction motor.

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