An appraisal of multi objective evolutionary algorithm for possible optimization of renewable energy systems - Igbinovia, Famous Oghomwen; Křupka, Jiří (IEEE (Institute of Electrical and Electronics Engineers), 2019)
The development of efficient multi objective evolutionary algorithms (MOEAs) can provide an effective tool for solving the optimization of solely renewable electricity systems. This paper presents an appraisal of multi objective evolutionary algorithms. It covers MOEA framework as a key issue in its design, these are including of MOEA based on decomposition, preference, indicator, hybridization, and co-evolution. Other MOEA frameworks covered in this paper are Target Region-based Multi Objective Evolutionary Algorithm (TMOEA) and Memetic Algorithm (MA) for multi objective evolutionary algorithms. The computational complexity of MOEAs has been presented. Potential direction for future research is in the area of MOEA application in an exclusively renewable energy system, thereby paving the way for the Internet of Renewable Energy (IoRE).
Posouzení více-objektivního evolučního algoritmu pro možnou optimalizaci systémů obnovitelné energie
Keywords: Computational complexity of multi objective evolutionary algorithm; evolutionary algorithm; multi objective evolutionary algorithm; multi objective evolutionary algorithm frameworks; renewable energy systems; Výpočtová složitost multiobjektivního evolučního algoritmu; evoluční algoritmus; multiobjektivní evoluční algoritmus; více objektivní evoluční algoritmové rámce; systémy obnovitelné energie