Automated Decision Optimization Techniques using Evolutionary and Swarm Intelligence Algorithms
Abstract
Evolutionary algorithms (EAs) and swarm intelligence (SI) algorithms constitute powerful, adaptive, and robust optimization frameworks inspired by natural and biological processes. These techniques are widely used for automated decision optimization in complex, multi‑objective, or non‑linear domains where classical optimization fails or impractical due to high dimensionality, non‑differentiability, or multimodality. EAs—such as Genetic Algorithms (GA), Differential Evolution (DE), Evolutionary Strategies (ES), and Co‑evolutionary frameworks—emulate biological evolution through mechanisms of selection, crossover, mutation, and fitness evaluation. Swarm algorithms—such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and other bio‑inspired methods—leverage collective behavior and information sharing in populations for distributed search and exploitation. The literature shows significant advances in algorithm designs, hybridization techniques, and novel applications in engineering, scheduling, machine learning, planning, and decision‑making. Key performance indicators include convergence speed, solution quality, robustness, parameter sensitivity, and scalability. This review synthesizes foundational theories, comparative studies, research methodologies, and application outcomes. It identifies trends, strengths, weaknesses, and practical challenges, providing insights for future work in hybrid techniques, multi‑objective frameworks, and real‑time decision automation. Science Publishing Corporation+2PubMed+2
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 4 No. 2 (2021): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
6340-6346 |
Published |
March 14, 2021 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sekhar Bandyopadhyay (2021). Automated Decision Optimization Techniques using Evolutionary and Swarm Intelligence Algorithms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 4 No. 2 (2021): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 6340-6346. https://doi.org/10.15662/IJAESIT.2021.0402001 |
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