چکیده:
In the science of operation research and decision theory, selection is the most important process. Selection is a process that studies multiple qualitative and quantitative criteria, related to the science of management, which are mostly incompatible with each other. The multi criteria selection of a renewable energy portfolio is one of the main issues considered in multi criteria literature. In the present study to form a portfolio of renewable energy, first, the KOHONEN neural network algorithm was used, and theneachportfolio was evaluated usingmulti criteriadecision-making methods. Further, throughMeta heuristicmulti objectivealgorithms Pareto rankanalysis was conducted andsocial acceptance ofrenewable energy production methods was assessed. Finally, the portfoliofor studied energies was composed. Theresults indicated that Cuckoo SearchAlgorithmand Grey Relational Analysis are effective and efficient for the selection of optimalParetoportfolioofrenewable energy.
خلاصه ماشینی:
In the present study to form a portfolio of renewable energy, first, the KOHONEN neural network algorithm was used, and then each portfolio was evaluated using multi criteria decision-making methods.
Further, through Meta heuristic multi objective algorithms Pareto rank analysis was conducted and social acceptance of renewable energy production methods was assessed.
The results indicated that Cuckoo Search Algorithm and Grey Relational Analysis are effective and efficient for the selection of optimal Pareto portfolio of renewable energy.
The goal of this study is to design such a multi objective programming model, using first, KOHONEN neural network, based on data envelopment analysis; second, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); third, Grey Relational Analysis; and, finally, VIKOR analysis, that will enable to determine the most reliable methods for the renewable energy production.
Finally the Pareto optimal solution ranking and socially acceptability of studied energy types are analyzed and examined in multi criteria selection mode using cuckoo, bees, moss and fireflies algorithms and biogeography based optimization.
Decrease the number of indexes using the feature selection algorithm Clustering the renewable energy production methods by using the Kohonen et Ranking of the energy production clusters Gray relational analysis Data envelopment analysis TOPSIS VIKOR method C1 C2 …….
Pareto analysis of rank and social acceptability of renewable energy projects: Pareto analysis was carried out using meta heuristic multi criteria algorithms including Cuckoo Search Algorithm, Bees Algorithm, Invasive Weed Optimization Algorithm, Firefly Algorithm and biogeography based optimization.