Abstract

ACOR is a well-established Ant Colony Optimization (ACO) algorithm for continuous-domain optimization. In this paper, we propose an extension (which we call ACOR∗) in which several fundamental modifications are made to ACOR's solution construction process, including the incorporation of a social influence mechanism borrowed from Particle Swarm Optimization (PSO). Our modifications to the ACOR algorithm are intended to promote search diversity and combat premature convergence. We experimentally evaluate our proposal in the context of training feedforward neural networks for classification using 65 widely used datasets from the University of California Irvine (UCI) repository, as well as the optimization of several popular synthetic continuous-domain benchmark functions, with number of dimensions varying up to 30,000. Our results empirically confirm that our proposal reduces the frequency of search stagnation, and improves performance on both applications, to a statistically significant extent.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

National Science Foundation, Grant 2420248

Keywords and Phrases

Estimation of Distribution Algorithms; Metaheuristics; Neural Network Training; Search Diversity; Statistical Significance; Swarm Intelligence

International Standard Serial Number (ISSN)

1865-9292; 1865-9284

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer, All rights reserved.

Publication Date

01 Jun 2026

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