An adaptive decomposition evolutionary algorithm based on environmental information for many-objective optimization.

The performance of traditional penalty boundary intersection (PBI) decomposition-based evolutionary algorithm is totally determined by the penalty factor. The fixed penalty factor causes the imbalance between the convergence and the diversity when solving many-objective problems. So, an adaptive decomposition evolutionary algorithm based on environmental information (MaOEA/ADEI) is proposed to solve the imbalance. The penalty factor of PBI decomposition is determined by the environmental information (include distribution information of weight vectors and population). Furthermore, the parent individual selection strategy is introduced to select promising individuals for variation and the weight vectors adaption strategy is used to handle problems with scaled objectives. Comparisons with 4 algorithms on 24 benchmark instances are used to test the property of MaOEA/ADEI. The experimental results show MaOEA/ADEI performs best on 14 test instances. • A convergence (CW) function is used to construct a mating pool in order to get offspring with good convergence performance. • The adaptive decomposition method based on environmental information is proposed to dynamically adjust the penalty factor of PBI. • The weight vectors adaptation strategy is used to produce uniformly distributed individuals specially the disparately scaled objectives. [ABSTRACT FROM AUTHOR]