Hybrid Optimization Based on Classical Evolution Strategies and Particle Swarm Optimization Applies to Car Racing Problem

Abstract

A hybrid optimization based on evolution strategies and particle swarm optimization is applied to the car racing problem. In this hybrid algorithm, individuals of each population are divided into two groups by fitness. The first (and better) group is based on evolution strategies , and the second group is based on particle swarm optimization, which are chosen mainly to take advantage of the balance between exploring and exploit. In order to find out the performance of the hybrid, the computer experiment is tested on both a standard test function set and a car racing problem.