  Although real-coded differential evolution (DE) algorithms can perform well
on continuous optimization problems (CoOPs), it is still a challenging task to
design an efficient binary-coded DE algorithm. Inspired by the learning
mechanism of particle swarm optimization (PSO) algorithms, we propose a binary
learning differential evolution (BLDE) algorithm that can efficiently locate
the global optimal solutions by learning from the last population. Then, we
theoretically prove the global convergence of BLDE, and compare it with some
existing binary-coded evolutionary algorithms (EAs) via numerical experiments.
Numerical results show that BLDE is competitive to the compared EAs, and
meanwhile, further study is performed via the change curves of a renewal metric
and a refinement metric to investigate why BLDE cannot outperform some compared
EAs for several selected benchmark problems. Finally, we employ BLDE solving
the unit commitment problem (UCP) in power systems to show its applicability in
practical problems.
