A Hybrid Capuchin Search Algorithm with Gradient Search Algorithm for Economic Dispatch Problem

Abstract Economic Load Dispatch (ELD) is an important task in power systems that is concerned with scheduling a set of thermal generating units to produce specific power at low consumption costs. In this task, there are some operational constraints related to power demand, power output, valve-point loading effect, transmission losses, prohibited operating zones, and ramp-rate limits. ELD is drafted as a non-linear, non-convex, and highly constrained optimization problem with a large number of local minima. Due to the complex and rugged search space of this problem, it has been tackled by many meta-heuristic algorithms. To cope with the above issues of this problem, we presented a novel Improved based Hybrid Capuchin Search Algorithm (IHCSA). First, a memory element was added to CSA to consolidate its position and velocity updating mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive functions rather than stationary social and cognitive parameters were used to manage and adequately balance exploration and exploitation of features over the iterations path. Finally, the hybridization was carried out using Gradient Search Algorithm (GBO) and Local Escaping Capability (LEO) to strengthen the intensification ability of CSA and balance its search capabilities to fulfill robust search performance. The proficiency of IHCSA was divulged by assessing it on a computationally difficult ELD problem under 6 different tests with different numbers of generators and loading conditions. The proposed IHCSA provided the best performance, in terms of the minimum cost, among the basic CSA and other optimization methods.