Test scheduling of System-on-Chip (SoC) is a major problem solved by various optimization techniques to minimize the cost and testing time. In this paper, we propose the application of Dragonfly and Ant Lion Optimization algorithms to minimize the test cost and test time of SoC. The swarm behavior of dragonfly and hunting behavior of Ant Lion optimization methods are used to optimize the scheduling time in the benchmark circuits. The proposed algorithms are tested on p22810 and d695 ITC’02 SoC benchmark circuits. The results of the proposed algorithms are compared with other algorithms like Ant Colony Optimization, Modified Ant Colony Optimization, Artificial Bee Colony, Modified Artificial Bee Colony, Firefly, Modified Firefly, and BAT algorithms to highlight the benefits of test time minimization. It is observed that the test time obtained for Dragonfly and Ant Lion optimization algorithms is 0.013188 Sec for D695, 0.013515 Sec for P22810, and 0.013432 Sec for D695, 0.013711 Sec for P22810 respectively with TAM Width of 64, which is less as compared to the other well-known optimization algorithms.

This article presents a novel Whale Optimization Algorithm (WOA) approach for optimal tuning of Fractional-Order Proportional Integral and integral-order Controller (FOPI^λ) is used to solar Photo-Voltaic (PV) fed sensorless speed control of Permanent Magnet Brushless DC (PMBLDC) motor under the various operating condition such as (i) speed change by set speed command signal, (ii) solar PV output voltage variations, (iii) varying load conditions, (iv) integrated conditions, and (v) controller parameters uncertainty. The PMBLDC motor is remarkably robust speed control against the uncertainties, Solar PV output power fluctuation, external load disturbances, the nonlinearity characteristics, and poor controllability. To overcome this above limitation, optimization algorithms are proposed namely Bat Algorithm (BA), Grey Wolf Optimization (GWO), and WOA optimized FOPI^λ controllers. To develop a MATLAB 2019a/Simulink model of FOPI^λ controller for Solar PV-fed sensorless speed control of PMBLDC motor using BA, GWO, and WOA and corresponding numerical simulation results are carried out under various working conditions. It shows the effectiveness of the proposed controllers is completely verified by comparing the above three intelligent optimization techniques. It is evident that out of these three intelligent controllers, the WOA-optimized FOPI^λ controller for solar PV-fed sensorless speed control of PMBLDC motor gives enhanced performance by minimizing the time domain parameters, performance indices error, convergence time, control efforts, cost function, mean, and standard deviation.