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The design and optimization of robust controller parameters are required to improve the controller performances and to keep the stability of load frequency control (LFC) system. In addition, reducing the number of iterations and computational time is very important for swiftly tuning of the controller parameters and the system to reach stability rapidly. For this purpose, this study presents the inserting of heuristic optimization techniques into stability regions method identified in proportional-integral (PI) controllers space for multiarea LFC systems with communication time delays (CTDs). This method consists of two steps: determination of stability region for the system and application of heuristics. Stability region for the system is found via stability boundary locus (SBL) and moth-flame optimization (MFO), particle swarm optimization (PSO), sine cosine algorithm (SCA), slime mould algorithm (SMA) and whale optimization algorithm (WOA) are inserted and applied to this region. In addition, a cost function having time domain specifications is developed for improving the performances of LFC and it is compared with the well-known integral error functions. Also, the robust stability region, which tolerates any system parameter and any time delay variation, is identified and the significance of this region is given for robustness analysis. It is observed from the analyses that better system outputs have been obtained with developed cost function. Steady state errors are minimized and transient state performances are improved with the proposed method. Moreover, desired system performances have been achieved with lower computational time and iteration number (approximately more than about 89% reduced according to classical approach) without deteriorating the stable structure of the system by the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel hybrid Taguchi-vortex search algorithm (HTVS) is proposed for solving global optimization problems. Taguchi orthogonal approximation and vortex search algorithm (VS) are hybridized in presenting method. In HTVS, orthogonal arrays in the Taguchi method are trained and obtained better solutions are used to find global optima in VS. Thus, HTVS has better relation between exploration and exploitation, and it exhibits more powerful approximation to find global optimum value. Proposed HTVS algorithm is applied to sixteen well-known benchmark optimization test functions with different dimensions. The results are compared with the Taguchi orthogonal array approximation (TOAA), vortex search algorithm, grey wolf optimizer (GWO), sine cosine algorithm (SCA), moth-flame optimization algorithm (MFO), whale optimization algorithm (WOA) and salp swarm algorithm (SSA). In order to compare the effectiveness of HTVS statistically, Wilcoxon signed-rank test (WSRT) is used in this study. Furthermore, HTVS is applied to two different real engineering problems having some constraints (tension/compression spring design and pressure vessel design). All obtained results suggested that HTVS can find optimal or very close to optimal results. Moreover, it has good computational ability and fast convergence behavior as well. [ABSTRACT FROM AUTHOR]