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<p>In order to solve complex optimization problems, swarm intelligence (SI) techniques that draw inspiration from the collective behavior of fish schools, ant foraging, and bird flocking are gaining popularity. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two widely recognized techniques in the fields of metaheuristics. This article provides a comprehensive examination of PSO and ACO, assessing their fundamental concepts, working mechanisms, algorithmic variations, and an extensive range of applications. We thoroughly compare the advantages and disadvantages of PSO and ACO, and examine their respective successes and failures in various scenarios. These approaches have demonstrated their effectiveness in practical scenarios, as evidenced by various case studies. This paper explores innovative advancements, ongoing challenges that require resolution, and thrilling new avenues for future research in swarm intelligence-based optimization. This paves the way for further advancements in this swiftly evolving domain.</p>

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Evolution of Swarm Intelligence: A Systematic Review of Particle Swarm and Ant Colony Optimization Approaches in Modern Research

  • 期刊论文

  • 发表日期：

    2025年3月18日

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  • Rahul Priyadarshi,
  • Ravi Ranjan Kumar

摘要

In order to solve complex optimization problems, swarm intelligence (SI) techniques that draw inspiration from the collective behavior of fish schools, ant foraging, and bird flocking are gaining popularity. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are two widely recognized techniques in the fields of metaheuristics. This article provides a comprehensive examination of PSO and ACO, assessing their fundamental concepts, working mechanisms, algorithmic variations, and an extensive range of applications. We thoroughly compare the advantages and disadvantages of PSO and ACO, and examine their respective successes and failures in various scenarios. These approaches have demonstrated their effectiveness in practical scenarios, as evidenced by various case studies. This paper explores innovative advancements, ongoing challenges that require resolution, and thrilling new avenues for future research in swarm intelligence-based optimization. This paves the way for further advancements in this swiftly evolving domain.