Abstract A combined prediction model based on long short-term memory neural network (LSTM) and convolutional (CNN) is proposed in order to increase the accuracy of load. To address issue that gray wolf optimization (GWO) search process prone falling into local optimum. An improved grey algorithm (IGWO) update convergence factor using lower incomplete gamma function improve global performance. T...

In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the latest bioinspired optimization techniques, which simulate the hunting process of grey wolves in nature. The binary version introduced here is performed using two different approaches. In the first app...

Economic load dispatch (ELD) provides significant benefits to the operation of power system. It appears be a complex nonconvex optimization problem subject several equal and unequal constraints. The greedy sine-cosine nonhierarchical gray wolf optimizer (G-SCNHGWO) is introduced in this study solve ELD problems efficiently robustly. sine cosine functions assist search agents grey (GWO) algorith...

In this paper, we present a set of optimal graphene-based multilayer surface plasmon resonance (SPR) biosensors for highly sensitive detection biomolecules. To optimize the biosensor structure, employed multi-objective gray wolf optimizer (MOGWO) to maximize sensitivity and minimize structure full width at half maximum (FWHM). The main advantages optimized structures are high sensitivity, low F...

Actually, path planning is one of the most crucial aspects mobile robots study. The primary goal this research to develop a solution issues that occur when “mobile robot” operates in static environment. problem handled by finding collision-free meets given criteria for shortest distance with quite smoothness path. Two nature-inspired metaheuristic algorithms are used computation. By leading hyb...