Despite the good performance of Crow Search Algorithm (CSA) in dealing with global optimization problems, unfortunately it is not the case with respect to the convergence performance. Conventional CSA exploration and exploitation are strongly dependent on the proper setting of awareness probability (AP) and flight length (FL) parameters. In each optimization problem, AP and FL parameters are set in an ad hoc manner and their values do not change over the optimization process. To this date, there is no analytical approach to adjust their best values. This presents a major drawback to apply CSA in complex practical problems. Hence, the conventional CSA is used only for limited problems due to fact that CSA with fixed AP and FL is frequently trapped into local optimum. In this present paper, an enhanced version of CSA called dynamic crow search algorithm (DCSA) is proposed to overcome the drawbacks of the conventional CSA. In the proposed DCSA, two modifications of the basic algorithm are made. The first modification concerns the continuous adjustment of the CSA parameters leading to a DCSA, where AP will be adjusting linearly over optimization process and FL will be adjusting according to the generalized Pareto probability density function. This dynamic adjustment will provide more global search capability as well as more exploitation of the pre-final solutions. The second modification concerns the improvement of CSA's swarm diversity in the search process. This will lead to a high convergence accuracy, and fast convergence rate. The effectiveness of the proposed algorithm is validated using a set of experimental series using 13 complex benchmark functions. Experimental results highly proved the modified algorithm effectiveness compared to the basic algorithm in terms of convergence rate, global search capability and final solutions. In addition, a comparison with conventional and recent similar algorithms revealed that DCSA gives superior results in terms of performance and efficiency.

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Poor control of the power grid can lead to a total system collapse, causing significant economic losses and possible damage to security and social peace. Therefore, improving power system stability, particularly transient stability, has become one of the major research topics. This paper proposes a developed modeling approach that provides the optimal stabilizer parameters of the control devices, aiming at improving the electrical network stability by minimizing the angular speed deviation in the presence of a severe disturbance event using a novel hybrid algorithm called Water Cycle-Moth Flame Optimization (WCMFO). The main advantages of the proposed method are the speed of response and its efficient exploration and exploitation ability to attain the best solution quality. This is achieved by imposing a thermodynamic incident (an abrupt change in mechanical torque) on the well-known test model (SMIB), Single Machine Infinite Bus. To test the effectiveness of the proposed method, Power System Stabilizer (PSS), Proportional-Integral-Derivative (PID-based PSS), and Fractional Order-PID (FOPID-based PSS) are implemented to control and ensure the system’s ability to return to a stable state in the presence of this fault. The achieved experimental outcomes have proven the superiority, and efficiency of the developed approach (WCMFO) in terms of damping the oscillations and reducing the overshot, with an improvement of 44% over the Water Cycle Algorithm (WCA), Moth-Flame Optimization (MFO), and Artificial Ecosystem Optimization (AEO). It is envisaged that the proposed method could be very useful in the design of a practical high-performance power system stabilizer.

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