Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

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 characteristics moths discover better flames all over search space. The exploration exploitation performances via are balanced by random parameter competing with criteria characteristic. A repair strategy working priority list created adjust any status simulation satisfy UC constraints. Then, Lambda-iteration adopted solve economic dispatch proficiency verified various scales systems short-term operational scheduling. results show yields solutions than do other methods. Wilcoxon signed rank test was conducted prove predominance BAMFO.