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 prime motive of economic load dispatch (ELD) is to optimize the production cost of electrical power generation through appropriate division of load demand among online generating units. Bio-inspired optimization algorithms have outperformed classical techniques for optimizing the production cost. Probability-selection artificial bee colony (PS-ABC) algorithm is a recently proposed variant of ABC optimization algorithm. PS-ABC generates optimal solutions using three different mutation equations simultaneously. The results show improved performance of PS-ABC over the ABC algorithm.

The simplicity and robustness of the Artificial Bee Colony (ABC) algorithm has attracted the attention of optimization researchers. Although ABC has fewer tuned parameters, making it an easy-to-use tool, it has shown better performance than other prominent optimization algorithms such as differential evolution (DE), evolutionary algorithms (EA) and particle swarm optimization (PSO) algorithms at solving optimization problems.

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes.