Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics

• URL: http://arxiv.org/abs/2508.00229v1
• Date: Fri, 01 Aug 2025 00:23:36 GMT
• Title: Sequential, Parallel and Consecutive Hybrid Evolutionary-Swarm Optimization Metaheuristics
• Authors: Piotr Urbańczyk, Aleksandra Urbańczyk, Magdalena Król, Leszek Rutkowski, Marek Kisiel-Dorohinicki,
• Abstract summary: This paper explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive manner.<n>The experimental results demonstrate that the hybrid approaches achieve superior convergence and consistency.<n>The paper introduces a novel consecutive hybrid PSO-GA evolutionary algorithm that ensures continuity between PSO and GA steps through explicit information transfer mechanisms.
• Score: 43.05659890525653
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Horizon-wise Learning Paradigm Promotes Gene Splicing Identification [6.225959701339916]
  We propose a novel framework for the task of gene splicing identification, named Horizon-wise Gene Splicing Identification (H-GSI) The proposed H-GSI follows the horizon-wise identification paradigm and comprises four components: the pre-processing procedure transforming string data into tensors, the sliding window technique handling long sequences, the SeqLab model, and the predictor. In contrast to existing studies that process gene information with a truncated fixed-length sequence, H-GSI employs a horizon-wise identification paradigm in which all positions in a sequence are predicted with only one forward computation.
  arXiv  Detail & Related papers  (2024-06-15T08:18:09Z)
• GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes [1.7226572355808027]
  We propose Gene Regulatory Genetic Algorithm (GRGA), which is the first time to utilize relationships among genes for improving GA's accuracy and efficiency. We use a directed multipartite graph encapsulating the solution space, called RGGR, where each node corresponds to a gene in the solution and the edge represents the relationship between adjacent nodes. The obtained RGGR is then employed to determine appropriate loci of crossover and mutation operators, thereby directing the evolutionary process toward faster and better convergence.
  arXiv  Detail & Related papers  (2024-04-28T08:33:39Z)
• Heterogeneous Multi-Task Gaussian Cox Processes [61.67344039414193]
  We present a novel extension of multi-task Gaussian Cox processes for modeling heterogeneous correlated tasks jointly. A MOGP prior over the parameters of the dedicated likelihoods for classification, regression and point process tasks can facilitate sharing of information between heterogeneous tasks. We derive a mean-field approximation to realize closed-form iterative updates for estimating model parameters.
  arXiv  Detail & Related papers  (2023-08-29T15:01:01Z)
• Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6 [55.41644538483948]
  This letter introduces two improved algorithms that significantly speed up the discovery of symmetry transformations. Given the significant complexity of the exceptional Lie groups, our results demonstrate that this machine-learning method for discovering symmetries is completely general and can be applied to a wide variety of labeled datasets.
  arXiv  Detail & Related papers  (2023-07-10T20:25:44Z)
• Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations [50.591267188664666]
  Propulate is an evolutionary optimization algorithm and software package for global optimization. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy.
  arXiv  Detail & Related papers  (2023-01-20T18:17:34Z)
• The Hybridization of Branch and Bound with Metaheuristics for Nonconvex Multiobjective Optimization [0.0]
  A hybrid framework combining the branch bound method with multiobjective evolutionary algorithms is proposed. A multiobjective evolutionary algorithm is intended for inducing tight lower and upper bounds during the branch and bound procedure.
  arXiv  Detail & Related papers  (2022-12-09T01:36:20Z)
• Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems [56.821213236215634]
  This paper focuses on a proposed hybrid version of the EMAS. It covers selection and introduction of a number of hybrid operators and defining rules for starting the hybrid steps of the main algorithm. Those hybrid steps leverage existing, well-known and proven to be efficient metaheuristics, and integrate their results into the main algorithm.
  arXiv  Detail & Related papers  (2022-10-24T13:28:35Z)
• NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer [45.47667026025716]
  We propose a novel, robust and accelerated iteration that relies on two key elements. The convergence and stability of the obtained method, referred to as NAG-GS, are first studied extensively. We show that NAG-arity is competitive with state-the-art methods such as momentum SGD with weight decay and AdamW for the training of machine learning models.
  arXiv  Detail & Related papers  (2022-09-29T16:54:53Z)
• Chaos inspired Particle Swarm Optimization with Levy Flight for Genome Sequence Assembly [0.0]
  In this paper, we propose a new variant of PSO to address the permutation-optimization problem. PSO is integrated with the Chaos and Levy Flight (A random walk algorithm) to effectively balance the exploration and exploitation capability of the algorithm. Empirical experiments are conducted to evaluate the performance of the proposed method in comparison to the other variants of PSO proposed in the literature.
  arXiv  Detail & Related papers  (2021-10-20T15:24:27Z)
• Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems [1.0323063834827413]
  This paper discusses a new variant of the Henry Gas Solubility Optimization (HGSO) Algorithm, called Hybrid HGSO (HHGSO) Unlike its predecessor, HHGSO allows multiple clusters serving different individual meta-heuristic algorithms to coexist within the same population. Exploiting the dynamic cluster-to-algorithm mapping via penalized and reward model with adaptive switching factor, HHGSO offers a novel approach for meta-heuristic hybridization.
  arXiv  Detail & Related papers  (2021-05-31T12:42:15Z)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.