Artificial Bee Colony Differential Evolution Research Articles

Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer

On the bases of meta-heuristic optimizers and experimental datasets, the parameter extraction of the proton exchange membrane fuel cells (PEMFCs) model to reach accurate current/voltage (I/V) curves remain an active research area during these last years. In this paper, an improved hybridized optimizer is developed to accurately estimate the PEMFC model parameters namely artificial bee colony differential evolution optimizer (ABCDE). In the developed ABCDE, the double execution of the mutation strategy allows enhancing the exploitation phase and avoiding to get stuck into the local minima. To assess the proposed ABCDE based parameter's identification, a comparative study with the recently published techniques including shuffled complex evolution, artificial ecosystem-based optimizer, and enhanced Lévy flight bat algorithm is performed using five typical PEMFCs modules. In this context, the reached sum of squared errors (SSE) and the standard deviations (STD) are very competitive among the challenging methodologies. ABCDE reaches the best SSE values within interesting overall STD and CPU run time less than 3e−15 and 0.225 s, respectively, for the five cases under study. It can be confirmed that the cropped SSE values and the STD among other challenging methodologies are very competitive with the best convergence speed. The ABCDE reaches 0.011697781, 2.07916558, 0.85360752, 9.6536060e−02, and 1.42098181379214e−04 for BCS 500W, NedStack PS6, Ballard Mark V, Horizon H-12, and Modular SR-12; respectively. In addition to that, the comparison results indicate that the proposed ABCDE is successfully utilized to characterize the PEMFC's model reliably and rapidly.

Accurate PEM fuel cells parameters estimation using hybrid artificial bee colony differential evolution shuffled complex optimizer

To get efficient current/voltage (I/V) polarization curves, the parameter identification of the proton exchange membrane (PEM) fuel cells (FCs) model based on experimental datasets and meta-heuristic algorithms remains an active research field during the past few years. Meanwhile, estimating those parameters accurately is still a challenge. In this work, a new hybridized approach is presented to identify the PEMFC model parameters denominated the artificial bee colony differential evolution shuffled complex (ABCDESC) optimizer. In the proposed algorithm, the double execution of the probabilities evaluation and the selection strategy of ABC optimizer enables to have better exploitation phase without getting stuck into the local optimum. The sum of squared errors (SSE)-based objective function is used to perform the optimization as it is the well used in the literature. In order to assess this new hybridized approach, a comparative study with the latest published techniques is carried out using six typical test benchmarking PEMFCs modules extensively utilized in the literature. In this context, the reached SSE values and the standard deviations among other challenging methodologies are very competitive with the best convergence speed and reduced number of function evaluations. The ABCDESC algorithm reaches a standard deviation/CPU run time (STD/CRT) of 8.4690e−16/0.298 second, 1.3275e−16/0.290 second, 1.9721e−14/0.356 second, 4.3889e−15/0.343 second, 1.4388e−12/0.49 second, and 1.6398e−17/0.326 second for 250 W, BCS 500 W, NedStack PS6, Ballard Mark V, Horizon H-12, and Modular SR-12 stacks; respectively. The comparison results indicate the successful use of the proposed ABCDESC optimizer to characterize the PEMFC model accurately.

Application of multi-objective hybrid artificial bee colony with differential evolution algorithm for optimal placement of microprocessor based FACTS controllers

Increasing electricity demand, financial concerns for building new power stations have resulted in overloading, extreme power transfer, huge losses, reduced power eminence, consistency issues and voltage contour issues. To overcome these issues Flexible AC Transmission System (FACTS) controllers can be integrated into the transmission systems. Subsequently, it improves stagnant and vigorous enactment. Conversely, site, type and capacity of FACTS controllers have to be optimized to attain the aforesaid objectives. In this study, multiple objectives such as minimization of voltage deviation, losses, cost, and line loading index have been optimized using hybrid Artificial Bee Colony-Differential Evolution (ABC-DE). Three microprocessor controlled FACTS controllers for instance thyristor controlled series compensator (TCSC), static VAR compensator (SVC) and unified power flow controller (UPFC) have been placed optimally on IEEE 30 bus system. The outcomes of hybrid ABC-DE have been validated with the outcomes of particle swarm optimization (PSO). It implies that hybrid ABC/DE outperformed PSO for allocating FACTS controllers by optimizing multiple objectives concurrently.

Reactive Power Optimization Using Hybrid CABC-DE Algorithm

Reactive power optimization is closely related to voltage quality and network loss, and it has great significance for the safety, reliability, and economical operation of the power system. Differential evolution (DE) algorithm has been currently applied to reactive power optimization. In order to mitigate the shortcomings of poor local search ability and premature convergence in DE, this paper presents a novel hybrid algorithm–chaotic artificial bee colony differential evolution (CABC-DE) algorithm, which improves the DE algorithm based on artificial bee colony algorithm and ideas of chaotic search. It introduces the observation bees' acceleration operation and the detective bees' chaotic search operation into CABC-DE. The validity of the proposed method is examined using IEEE-14 and IEEE-30 bus system. The experimental results show that CABC-DE algorithm is more effective than regular DE algorithm for reactive power optimization. The algorithm can save the search time greatly and get a better solution for optimization, thus making it suitable for solving reactive power optimization problems.