The Comparison of Lately Proposed Harris Hawks Optimization and Jaya Optimization in Solving Directional Overcurrent Relays Coordination Problem

Abstract

In this paper, a lately proposed Harris Hawks Optimizer (HHO) is used to solve the directional overcurrent relays (DOCRs) coordination problem. To the best of the authors’ knowledge, this is the first time HHO is being used in the DOCRs coordination problem. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks from different directions, based on the dynamic nature of scenarios and escaping patterns of the prey. To test its performances in solving the DOCRs coordination problem, it is adopted in 3-bus, 4-bus, 8-bus, and 9-bus systems, which are formulated by three kinds of optimization models as linear programming (LP), nonlinear programming (NLP), and mixed integer nonlinear programming (MINLP), according to the nature of the design variables. Meanwhile, another lately proposed optimization algorithm named Jaya is also adopted to solve the same problem, and the results are compared with HHO in aspects of objective function value, convergence rate, robustness, and computation efficiency. The comparisons show that the robustness and consistency of HHO is relatively better than Jaya, while Jaya provides faster convergence rate with less CPU time and occasionally more competitive objective function value than HHO.

Multi-Response Optimization and Prediction of Tribological behaviours of Filled Polytetrafluoroethylene Composites Using Taguchi-Deng and Evolutionary Harris Hawk’s Optimization Methodologies

“…This implies SVR-HHO model performed better than SVR-PSO model for prediction of the tribological behaviours of the filled PTFE composites. The predictive superiority of HHO to others is in concord with results obtained by 45 . Fig.…”

Optimizing multiple ONUs placement in Fiber-Wireless (FiWi) access network using Grasshopper and Harris Hawks Optimization Algorithms

“…For higher value of it, the time of calculation increases and results in degraded computational efficiency. This feature of HHO algorithm becomes a challenge to solve real-world problems and to determine the most perfect value of the scale [39] . Further In [23] , the authors proposed GOA and tested its efficiency on various platforms.…”

“…The results of HHO algorithm report that it is very effective with meta -heuristic optimizers and show superiority over conventional techniques. Several authors used HHO algorithm for various applications in engineering [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] . The performance of HHO algorithm has been tested with other optimizers in [22] .…”

“…The performance of HHO algorithm has been tested with other optimizers in [22] . It has been successfully used for the design of microchannel heat sinks [38] , for solving the coordination problem in the directional over current relays (DOCRs) [39] , for extracting the unknown parameters of the three-diode photovoltaic (TDPV) model [40] , for manufacturing optimization problems [41] , for accurate and reliable air pollutant forecasting model [42] , for solving reference point multi-objective problems [43] , for image processing [44] , for satellite image segmentation [45] etc. In [46] , the authors proposed hybrid models by combining artificial neural network (ANN) with GOA and HHO for predicting soil compression coefficient (SCC).…”

An Effective Coordination Setting for Directional Overcurrent Relays Using Modified Harris Hawk Optimization

“…The optimized parameters for a relay coordination scheme are shown in Table 4. The results of MHHO are compared with baseline HHO [57] and other algorithms: the seeker algorithm (SA) [58], genetic algorithm (GA), genetic algorithm-linear programming (GA-LP) [59], biogeography-based optimization (BBO), biogeography-based optimization linear programming (BBO-LP) [56] and JAYA [45] algorithms. The objective function value of MHHO was compared with HHO and other algorithms.…”