Abstract
A hybrid optimization based on (μ,λ)-ES and particle
swarm intelligence is proposed. In this new hybrid algorithm,
individuals of each population are divided into two groups by
fitness. The first (better) group is dealt with (μ,λ) mutation,
and the second group is dealt with particle swarm intelligence.
Experiments are done on a set of standard benchmark functions.
In order to find out the performance of the hybrid, we test
different population divide ratios. Experimental results show
that hybrid optimization performs better than (μ,λ)-ES on
all the benchmark functions and better than particle swarm
optimization on part of the function set.