Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems

Salil Madhav Dubey, Hari Mohan Dubey and Surender Reddy Salkuti ()
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Salil Madhav Dubey: Department of Electrical Engineering, Madhav Institute of Technology & Science (MITS), Gwalior 474005, India
Hari Mohan Dubey: Department of Electrical Engineering, Birsa Institute of Technology Sindri (BIT Sindri), Sindri, Dhanbad 828123, India
Surender Reddy Salkuti: Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Korea

Energies, 2022, vol. 15, issue 15, 1-29

Abstract: This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems.

Keywords: quasi-opposed learning; grey wolf optimizer; mathematical benchmark; electrical benchmark; box plot analysis; microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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