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

A binary differential evolution algorithm learning from explored solutions