Details

Autor(en) / Beteiligte

• Liu, Tianping
• Xiong, Guojiang
• Wagdy Mohamed, Ali
• Nagaratnam Suganthan, Ponnuthurai

Titel

Opposition-mutual learning differential evolution with hybrid mutation strategy for large-scale economic load dispatch problems with valve-point effects and multi-fuel options

Ist Teil von

• Information sciences, 2022-09, Vol.609, p.1721-1745

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •An improved differential evolution (OMLIDE) for ELD problems is proposed.•An oppositional mutual learning strategy is used for population initialization.•Two novel mutation operators are hybridized adaptively.•Both scaling factor and crossover rate are adjusted automatically.•Benchmark functions of CEC2014 and five ELD cases are solved. The economic load dispatch (ELD) problem plays a crucial role in power system operation. In practice, the ELD problem becomes a non-convex, multi-constraint, non-linear optimization problem when considering the valve point effects, the prohibited operation zones, the ramp rate limit, and the multi-fuel options. To effectively solve this problem, this paper puts forward an improved differential evolution (DE) named OMLIDE based on opposition-mutual learning, hybrid mutation strategy, and parameters adaptive mechanism. OMLIDE differs from the traditional DE in that: (1) an opposition-mutual learning strategy is employed for population initialization to increase the probability of finding an optimal solution; (2) two novel mutation operators named DE/elite-to-ordinary/1 and DE/elite-to-ordinary/2 are hybridized and a selection probability is introduced to regulate them adaptively at different evolutionary stages; and (3) a parameters adaptive mechanism is presented to adjust the scaling factor and crossover rate. The proposed OMLIDE is first validated by the numerical benchmark functions of CEC 2014. Then it is applied to five non-convex ELD problems with valve-point effects and multi-fuel options. Simulation results demonstrate that OMLIDE provides better or highly competitive results in different terms compared with other peer algorithms.