This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame (MFO), the proposed is called alternative MFO (BAMFO). Originally, each moth restricted to fly toward predetermined flame (solution), causing it be trapped in local optimum of that and reducing its performance. To address these drawbacks, BAMFO represents four characterist...

The moth-flame optimization (MFO) algorithm is an effective nature-inspired based on the chemical effect of light moths as animal with bilateral symmetry. Although it widely used to solve different problems, its movement strategy affects convergence and balance between exploration exploitation when dealing complex problems. Since strategies significantly affect performance algorithms, use multi...

Extreme Learning Machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes to be ill-conditioned reduces its classification accuracy. The contribution of work is enhancement performance ELM with Moth-Flame Optimization (MFO) improve A hybrid (MFO-ELM) implemented MATLAB. MFO ensures concurrent simulation exploration exploitation search space s...

How to accurately and efficiently extract photovoltaic (PV) model parameters is the primary problem of system optimization. To PV models, an enhanced moth-flame optimization (EMFO) with multiple flame guidance mechanism proposed in this study. In EMFO, adaptive number updating used adaptively control number, which enhances local global exploration capabilities MFO. Meanwhile, a designed for ful...