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This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard realparameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixedinteger RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) algorithm is a population-based algorithm designed for global optimization of the optimization problems. This paper proposes a different DE algorithm based on mathematical modeling of socio-political evolution which is called Colonial Competitive Differential Evolution (CCDE). The two typical CCDE algorithms are benchmarked on three well-known test functions, and the results are verified by a comparative study with two original DE algorithms which include DE/best/1 and DE/rand/2. Also, the effectiveness of CCDE algorithms is tested on Economic Load Dispatch (ELD) problem including 10, 15, 40, and 140-unit test systems. In this study, the constraints and operational limitations, such as valve-point loading, transmission losses, ramp rate limits, and prohibited operating zones are considered. The comparative results show that the CCDE algorithms have good performance and are reliable tools in solving ELD problem. [ABSTRACT FROM AUTHOR]

The paper presents a novel teaching–learning-based optimization (TLBO) algorithm, the Gaussian bare-bones TLBO (GBTLBO) algorithm, with its modified version (MGBTLBO) for the optimal reactive power dispatch (ORPD) problem with discrete and continuous control variables in the standard IEEE power systems for reduction in power transmission loss. The feasibility and performance of the GBTLBO and MGBTLBO algorithms are demonstrated for standard IEEE 14-bus and standard IEEE 30-bus systems. A comparison of simulation results reveals optimization efficacy of the GBTLBO and MGBTLBO algorithms over other well established other algorithms like bare-bones differential evolution (BBDE) and bare-bones particle swarm optimization (BBPSO) algorithm. Results for ORPD problem demonstrate superiority in terms of solution quality of the GBTLBO and MGBTLBO algorithms over original TLBO algorithm and other algorithm. [ABSTRACT FROM AUTHOR]