PSO-Tuned Control Parameter in Differential Evolution Algorithm

• Published in: Swarm, Evolutionary, and Memetic Computing Vol. 7677; pp. 417 - 424
• Main Authors: Si, Tapas, Jana, Nanda Dulal, Sil, Jaya
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2012; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• ISBN: 3642353797; 9783642353796
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-35380-2_49

In this work, a method to control the parameters of Differential Evolution (DE) algorithm is proposed. Here the control parameters of DE are co-evolved by Particle Swarm Optimization (PSO) algorithm. The classical DE algorithm has two main control parameters: Scale Factor (F) and Cross-over Rate (CR). These are selected on trial-and-error basis for solving optimization problems. Several optimization problems lead to optimal or sub-optimal solution by proper selection of control parameters of the DE algorithm. In this proposed method, PSO algorithm is used to tune the scale factor and cross-over rate in DE algorithm. Basically PSO algorithm is used as a meta-optimizer for DE algorithm. The proposed method is termed as mPSO-DE in this paper. The mPSO-DE algorithm is applied on 12 benchmark unconstrained optimization problems. The obtained results are compared with that of classical DE algorithm. From the experimental studies, it has been found that the proposed mPSO-DE algorithm performed better than DE algorithm.