Heterogeneous Differential Evolution for Numerical Optimization

Abstract EN

Differential evolution (DE) is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems.

Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme.

This paper proposes a simple and effective heterogeneous DE (HDE) to balance exploration and exploitation.

In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool.

Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems.

The results show that heterogeneous DE achieves promising performance on a majority of the test problems.