Hybrid Optimization based on (\mu, \lambda)-Evolution Strategies and Particle Swarm Intelligence

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.