Performance Analysis and Improvement of Parallel Differential Evolution
This work addresses the computational efficiency problem for researchers and practitioners using DE in large-scale optimization, though it is incremental as it modifies an existing operator for better parallelism.
The paper tackled the computational performance bottleneck of Differential Evolution (DE) for large-scale problems by proposing a new exponential crossover operator (NEC) that enables efficient parallel execution with MKL/CUDA, resulting in significant speed improvements as shown in experiments.
Differential evolution (DE) is an effective global evolutionary optimization algorithm using to solve global optimization problems mainly in a continuous domain. In this field, researchers pay more attention to improving the capability of DE to find better global solutions, however, the computational performance of DE is also a very interesting aspect especially when the problem scale is quite large. Firstly, this paper analyzes the design of parallel computation of DE which can easily be executed in Math Kernel Library (MKL) and Compute Unified Device Architecture (CUDA). Then the essence of the exponential crossover operator is described and we point out that it cannot be used for better parallel computation. Later, we propose a new exponential crossover operator (NEC) that can be executed parallelly with MKL/CUDA. Next, the extended experiments show that the new crossover operator can speed up DE greatly. In the end, we test the new parallel DE structure, illustrating that the former is much faster.