Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics

TL;DR

This paper investigates hybrid evolutionary-swarm algorithms combining PSO and GA in various configurations, demonstrating improved convergence on benchmark functions, and introduces a novel consecutive hybrid PSO-GA method with explicit information transfer.

Contribution

It presents a comprehensive comparison of hybrid PSO-GA metaheuristics and introduces a new consecutive hybrid algorithm with enhanced information sharing.

Findings

Hybrid approaches outperform basic algorithms in convergence.

Hybrid methods show better performance in high-dimensional spaces.

The new consecutive hybrid PSO-GA improves information transfer.

Abstract

The goal of this paper is twofold. First, it explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive manner in comparison with their standard basic form: Genetic Algorithm and Particle Swarm Optimization. The algorithms were tested on a set of benchmark functions, including Ackley, Griewank, Levy, Michalewicz, Rastrigin, Schwefel, and Shifted Rotated Weierstrass, across multiple dimensions. The experimental results demonstrate that the hybrid approaches achieve superior convergence and consistency, especially in higher-dimensional search spaces. The second goal of this paper is to introduce a novel consecutive hybrid PSO-GA evolutionary algorithm that ensures continuity between PSO and GA steps through explicit information transfer mechanisms, specifically by modifying GA's variation operators to inherit velocity and personal best information.