Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics

2025-07-31

AI Summary

To address the limitations of single metaheuristic algorithms—such as premature convergence and slow convergence—in high-dimensional complex function optimization, this paper proposes three hybrid PSO-GA paradigms: sequential, parallel, and continuous. A novel continuous hybrid PSO-GA algorithm is specifically designed, featuring an explicit information transfer mechanism that incorporates PSO’s velocity vectors and personal best positions into GA’s mutation and selection operators, thereby enhancing population diversity and search continuity. Experimental evaluation on high-dimensional benchmark functions (e.g., Ackley, Rastrigin) demonstrates that the proposed method significantly outperforms standard PSO and GA in both convergence speed and stability. Notably, its superiority becomes more pronounced in ultra-high-dimensional settings (≥100D), validating the effectiveness and robustness of cross-paradigm synergistic optimization.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

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.

Problem

• Hybrid evolutionary-swarm metaheuristics combining PSO and GA
• Comparison of sequential, parallel, consecutive hybrids with standard GA and PSO
• Novel consecutive hybrid PSO-GA algorithm with information transfer mechanisms

Innovation

• Hybrid PSO-GA in sequential, parallel, consecutive
• Modified GA inherits PSO velocity and best
• Superior convergence in high-dimensional search spaces