Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model
Апстракт
Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evoluti...onary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO out-performs other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs.
Кључне речи:
Whale Optimization Algorithm / Renewable energy systems / Planning control / Ordinal priority approach / Optimization / Decision -making analysis / Algorithm / Absolute optimalИзвор:
Expert Systems with Applications, 2023, 221Издавач:
- Pergamon-Elsevier Science Ltd, Oxford
DOI: 10.1016/j.eswa.2023.119731
ISSN: 0957-4174
WoS: 000949951800001
Scopus: 2-s2.0-85149187385
Институција/група
Fakultet organizacionih naukaTY - JOUR AU - Ala, Ali AU - Mahmoudi, Amin AU - Mirjalili, Seyedali AU - Simić, Vladimir AU - Pamučar, Dragan PY - 2023 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2507 AB - Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO out-performs other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs. PB - Pergamon-Elsevier Science Ltd, Oxford T2 - Expert Systems with Applications T1 - Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model VL - 221 DO - 10.1016/j.eswa.2023.119731 UR - conv_2890 ER -
@article{ author = "Ala, Ali and Mahmoudi, Amin and Mirjalili, Seyedali and Simić, Vladimir and Pamučar, Dragan", year = "2023", abstract = "Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO out-performs other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs.", publisher = "Pergamon-Elsevier Science Ltd, Oxford", journal = "Expert Systems with Applications", title = "Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model", volume = "221", doi = "10.1016/j.eswa.2023.119731", url = "conv_2890" }
Ala, A., Mahmoudi, A., Mirjalili, S., Simić, V.,& Pamučar, D.. (2023). Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model. in Expert Systems with Applications Pergamon-Elsevier Science Ltd, Oxford., 221. https://doi.org/10.1016/j.eswa.2023.119731 conv_2890
Ala A, Mahmoudi A, Mirjalili S, Simić V, Pamučar D. Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model. in Expert Systems with Applications. 2023;221. doi:10.1016/j.eswa.2023.119731 conv_2890 .
Ala, Ali, Mahmoudi, Amin, Mirjalili, Seyedali, Simić, Vladimir, Pamučar, Dragan, "Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model" in Expert Systems with Applications, 221 (2023), https://doi.org/10.1016/j.eswa.2023.119731 ., conv_2890 .