Artificial neural network regression as a local search heuristic for ensemble strategies in differential evolution

作者：Fister Iztok; Suganthan Ponnuthurai Nagaratnam; Fister Iztok Jr; Kamal Salahuddin M; Al Marzouki Fahad M; Perc Matjaz^*; Strnad Damjan

来源：Nonlinear Dynamics, 2016, 84(2): 895-914.

DOI：10.1007/s11071-015-2537-8

• Nonlinear dynamics
• Artificial neural network
• Differential evolution
• Regression
• Local search
• Ensemble strategies

Nature frequently serves as an inspiration for developing new algorithms to solve challenging real-world problems. Mathematical modeling has led to the development of artificial neural networks (ANNs), which have proven especially useful for solving problems such as classification and regression. Moreover, evolutionary algorithms (EAs), inspired by Darwin's natural evolution, have been successfully applied to solve optimization, modeling, and simulation problems. Differential evolution (DE) is a particularly well-known EA that possesses a multitude of strategies for generating an offspring solution, where the best strategy is not known in advance. In this paper, the ANN regression is applied as a local search heuristic within the DE algorithm that tries predicting the best strategy or attempting to generate a better offspring from an ensemble of DE strategies. This local search heuristic is applied to the population of solutions according to a control parameter that regulates between the time complexity of calculation and the quality of the solution. The experiments on a CEC 2014 test suite consisting of 30 benchmark functions reveal the full potential in developing this idea.

• 出版日期2016-4
• 单位南阳理工学院