An Effective Quantum Inspired Genetic Algorithm for Continuous Multiobjective Optimization

Multiobjective Optimization Problems (MOP) can be found in many issues of scientific research, engineering, and in everyday social life. A MOP problem has several objectives that conflict with one another which must be optimized simultaneously. This paper presents a quantum-inspired evolutionary alg...

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Vydáno v:2019 5th International Conference on Science in Information Technology (ICSITech) s. 161 - 166
Hlavní autoři: Abraham, Julisa Bana, Wahyunggoro, Oyas, Setiawan, Noor Akhmad
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2019
Témata:
Evolutionary computation
Fast Nondominated Sorting
Genetic Algorithm
Genetic algorithms
Information technology
Logic gates
Measurement
Optimization
Quantum Inspired Evolutionary Algorithm
Sorting
Testing
ISBN:9781728123783, 172812378X
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Shrnutí:Multiobjective Optimization Problems (MOP) can be found in many issues of scientific research, engineering, and in everyday social life. A MOP problem has several objectives that conflict with one another which must be optimized simultaneously. This paper presents a quantum-inspired evolutionary algorithm (QEA) to solve continuous multiobjective optimization problem (MOP). The proposed method employs Fast Nondominated Sorting and Crowding Distance from NSGA-II and implements all common operators of genetic algorithms (GA), such as crossover and mutations with additional Quantum Gate quantum operators. The proposed method is then run in a distributed manner and is proven to be able to significantly outperform the hypervolume and MOEA/D metrics and have hypervolumes that are comparable to NSGA-II while maintaining a better average Δ' in all testing problems. From this result, it is concluded that using quantum-inspired individual genetic algorithms to solve continuous MOP can produce hypervolume and Δ' metrics that are good in all specified test problems.
ISBN:9781728123783
172812378X
DOI:10.1109/ICSITech46713.2019.8987578