Multiple-global-best guided artificial bee colony algorithm for induction motor parameter estimation

Abstract

Abstract:An induction motor is the most commonly used motor in industry today. Motor circuit parameters are essential for designing, evaluating performance, and controlling the applications of the motor. However, it is difficult to measure the electric parameters, e.g., resistance and reactance, of induction motors accurately. Therefore, researchers have noted the parameter estimation of induction motors as an essential optimization problem. The artificial bee colony (ABC) algorithm is an efficient element of bioinspired optimization algorithms and has been successfully applied in numerous engineering applications. However, the ABC algorithm suffers from slow convergence and poor exploitation. Additionally, there are bleak chances of getting a fitter food source for scout bees using the the standard ABC algorithm scheme.Therefore, different solutions have already been proposed to avoid the flaws of the ABC algorithm. Nevertheless, the proposed solutions are either computationally intensive or prone to local optima traps or they require additional control variables to tune. Moreover, there is no systematic way to tune the additional control variables for yielding the optimal performance of the algorithms. Therefore, this research work proposes a novel variant of the ABC algorithm, which capitalizes on multiple global-best food sources rather than a single global-best food source. In addition, this research work proposes a novel scheme for enhancing the performance of the ABC algorithm's scout bee. Two modifications for the performance enhancement of the ABC algorithm are proposed in this research work. The proposed algorithm is compared with various recently proposed variants of the ABC algorithm and various other available methods for estimating induction motor parameters. The performance of the proposed algorithm is also assessed using the chaotic map initialization technique. The results prove that the proposed algorithm is able to achieve the best convergence among all of the compared algorithms.

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

“…Different from most of the existent evolutionary algorithms, the GSA presents a better performance in multimodal problems, avoiding critical flaws such as the premature convergence to sub-optimal solutions [13,14]. In the GSA, candidate solutions emulate masses which attract each other through operators that mimic the gravitational force.…”

Software Cost Estimation using Enhanced Artificial Bee Colony Algorithm

“…On the other hand, it suffers from slow convergence and tendency to local optima [16]. Researchers have been proposed to overcome these drawbacks [2], [16]- [20]. Abro et al [2] proposed a novel variant of basic ABC algorithm which is called Enhanced-ABC algorithm (E-ABC).…”

“…Researchers have been proposed to overcome these drawbacks [2], [16]- [20]. Abro et al [2] proposed a novel variant of basic ABC algorithm which is called Enhanced-ABC algorithm (E-ABC). The E-ABC algorithm uses multiple global-best possiblesolutions (GBPS) rather than single global-best possiblesolution in exploration and exploitation phase.…”

“…After she finds a food source, she tries to explore the multiple best-found locations of search space instead of the neighborhood of an assigned food source like in basic ABC algorithm. (6) where is the produced new source from which represents the old food source, is randomly selected number between (-1,1) [2]. represents one of the high-nectar-content food sources.…”

“…Algorithmic models, one of the successful and simple software cost estimation techniques, use mathematical formulas that are based on the effort in terms of person-months at various life cycles of the software for a project [1]. In this study, we implement enhanced artificial bee colony method proposed in [2] to establish an algorithmic model for estimating software cost.…”