New self-adaptive bat-inspired algorithm for unit commitment problem

Bat-inspired algorithm (BA) is a new evolutionary meta-heuristics algorithm inspired by a known technique of bats for finding prey. This study presents a self-adaptive BA to solve the unit commitment (UC) problem. The applied self-adaptive technique increases the population diversity and improves the exploration power of BA which results in better solutions and higher speed of convergence in solving the UC problem. This study, also, applies simple methods to handle the minimum on-/off-time constraint and spinning reserve requirement in generation of all solutions directly and without using any penalty function. The performance of the proposed method is verified by applying 10 up to 100-unit systems as well as a Taiwan power (Taipower) 38-unit system in a 24 h scheduling horizon.