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Bio-Inspired Algorithms: 2nd Edition

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Special Issue Editors

Special Issue Information

Dear Colleagues,

In the field of applied informatics, the algorithmic-based procedural approach has distinguised itself with indisputable advantages, but it also has several limitations with respect to hard problems without exact solutions due to incomplete or imperfect information and high computation demands.

It is frequently worth looking towards biology to better understand and model solutions for complex real-world problems. Nature is a great source of inspiration for optimization methods for solving large, indeterministic, inscrutable problems for which information is lacking. Several efficient methods and method groups are based on the process of natural selection, the behavior of living creatures (or groups of living creatures), physical phenomena, or, notably, on the mechanisms of the brain.

For this Special Issue, "Bio-Inspired Algorithms: 2nd Edition", we seek original research papers about novel, bio-inspired methods, analyses of already-existing techniques, or high-level practical applications from the field of computer science or any interdisciplinary field. We welcome manuscripts discussing evolutional (Genetic Algorithms, NSGA, etc.), swarm-intelligence-based (Particle Swarm Optimization, Ant Colony Optimization, the Fireworks Algorithm, etc.), or brain-inspired computing (Neural Networks, Deep Learning, etc.) methods applied in any kind of research project (image processing, natural language processing, general optimization, physical simulations, etc.).

Prof. Dr. Sándor Szénási
Dr. Gábor Kertész
Guest Editors

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Keywords

design and analysis of bio-inspired methods
application of bio-inspired methods
limitations of bio-inspired methods
ant colony optimization
particle swarm optimization
firefly algorithm
fireworks algorithm
bees algorithm
evolutionary algorithms
neural networks
deep learning
soft computing methods
nature-inspired heuristics

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15 pages, 356 KiB

Open AccessArticle

Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics

by Sándor Szénási, Gábor Légrádi and Gábor Kovács

Algorithms 2025, 18(5), 298; https://doi.org/10.3390/a18050298 - 21 May 2025

Viewed by 132

Abstract

Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming and complex task. This paper proposes a hybrid variation of the Hill Climbing method using a Machine Learning model to learn this domain-specific knowledge in advance to help determine the optimal step size of each iteration. A Deep Feedforward Neural Network was trained on the steps of thousands of Hill Climbing runs. This model was used in a novel alternating method (using traditional and Machine Learning-based steps) to predict the optimal step size for each iteration. This hybrid algorithm was compared to the already-known variants. The results show that the novel hybrid method is able to find slightly better results than the original Hill Climbing method, requiring significantly fewer fitness calculations. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)

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