Best Member Detection and Using as Differential Evolution Crossover Operator in Decomposition-based Multiobjective Optimization Algorithm

Yıl 2022, , 52 - 67, 10.10.2022

Ökkeş Tolga Altınöz

https://doi.org/10.53070/bbd.1173588

Öz

Decomposition is a method to distributes a mutliobjective problems to the many single objective problems like scalarization. Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) is one of the many algorithms uses decomposition method. In MOEA/D algorithm genetic operators are preferred to alter the population. As one of the genetic operators, the crossover is an important element in the algorithm. Hence it is possible to propose new possible methods instead of well-known SBX method. Differential Evolution (DE) which is a single objective optimization algorithm can be used as crossover operator in MOEA/D. However, in DE the best member needed to be detected in the population. Even it is relatively easy in single objective, systematic methods are needed for this purpose. Therefore, in this research three different best member detection methodology will be compared in DE assist MOEA/D algorithm. These methods will be compared on benchmark problems with many objectives.

Anahtar Kelimeler

MOEA/D, decomposition, multiobjective optimization, crossover.

Kaynakça

• Altinoz, T. (2022) Comparing Simulated Crossover and Differential Evolution Crossover operator in MOEA/D Algorithm. 1st International Conference on Engineering and Applied Natural Sciences, pp. 1-6.
• Altinoz, T. (2022b) Decomposition Variants of the Multi-objective Evolutionary Algorithm Based on Decomposition Algorithm for Solving Many-objective Optimization Problems. 2nd. International Symposium of Scientific Research and Innovative Studies, pp. 1-10.
• Cheng, R. Li, M., Tian, Y. Zhang, X. Yang, S. Jin, Y. and Yao, X. (2017) A Benchmark Test Suit for Evolutionary Many-objective Optimization. Complex Intell. Syst. vol. 3, pp. 67–81.
• Deb, K. and Agrawal, R.B. (1995) Simulated binary crossover for continuous search space. Complex systems, vol. 9, no. 2, pp. 115-148.
• Ehrgott, e. (2000) Approximation algorithms for combinatorial multicriteria optimization problems. International Transactions in Operational Research, vol. 7, no. 531.
• Ishibuchi, H. Masuda, H. Tanigaki, Y. and Nojima, Y. (2015) Modified distance calculation in generational distance and inverted generational distance. International Conference on Evolutionary Multi-Criterion Optimization. Springer, pp. 110–125.
• Li, H. and Zhang, Q. (2009) Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284-302.
• Zhang Q. Li H. “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition,” IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, 2007.