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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]

This study presents a new MOMICA (Multi-Objective Modified Imperialist Competitive Algorithm) for the multi-objective OPF (optimal power flow) problem. The OPF problem can be solved for minimum generation cost which satisfies the power balance equations and system constraints. However, cost based OPF problem solutions usually result in unattractive system emission, losses and voltage profiles. In this paper, the fuel cost, emission, voltage deviation and active power losses impacts are considered as the objective functions for the proposed multi-objective OPF problem. The obtained final optimal solution using MOMICA is compared with that obtained using multi-objective algorithm in the literature. The performance of multi-objective algorithms is studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus power systems. The proposed MOMICA method provides better results compared with the other algorithm as demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, a reliable and effective algorithm based on hybrid modified imperialist competitive algorithm (MICA) and invasive weed optimization (IWO) is proposed for solving the optimal reactive power dispatch (ORPD) problem. Without doubt, one of the simple but powerful optimization algorithms in the field of evolutionary optimization is imperialist competitive algorithm (ICA) outperforming many of the already existing stochastic and direct search global optimization techniques. The original ICA method often converges to local optima. In order to avoid this shortcoming, a new method is propose that profits from IWO method to improve local search near the global best and a series of modifications is purposed to the assimilation policy rule of ICA in order to further enhance algorithm's rate of convergence for achieving a better solution quality. The hybrid MICA-IWO method is then offered for handling ORPD problem. The introduced method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems for testing and validation purposes. The hybrid MICA-IWO provides better results compared to the original ICA, IWO, and other methods reported in the literature as demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

Abstract: One of the major tools for power system operators is optimal power flow (OPF) which is an important tool in both planning and operating stages, designed to optimize a certain objective over power network variables under certain constraints. Without doubt one of the simple but powerful optimization algorithms in the field of evolutionary optimization is imperialist competitive algorithm (ICA); outperforming many of the already existing stochastic and direct search global optimization techniques. The original ICA method often converges to local optima. In order to avoid this shortcoming, we propose a new method that profits from teaching learning algorithm (TLA) to improve local search near the global best and a series of modifications is purposed to the assimilation policy rule of ICA in order to further enhance algorithm’s rate of convergence for achieving a better solution quality. This paper investigates the possibility of using recently emerged evolutionary-based approach as a solution for the OPF problem which is based on hybrid modified ICA (MICA) and TLA (MICA–TLA) for optimal settings of OPF control variables. The performance of this approach is studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus test systems with different objective functions and is compared to methods reported in the literature. The hybrid MICA–TLA provides better results compared to the original ICA, TLA, MICA, and other methods reported in the literature as demonstrated by simulation results. [Copyright &y& Elsevier]

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]