A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain

Area of Science:

• Robotics and Artificial Intelligence
• Computational Intelligence
• Optimization Algorithms

Background:

• Swarm Intelligence (SI) offers decentralized collective behavior suitable for swarm robotics.
• Particle Swarm Optimization (PSO) is a popular SI algorithm for optimization, particularly in search strategies.
• PSO's simplicity, effectiveness, and low computational cost make it ideal for swarm robotics applications.

Purpose of the Study:

• To systematically review and analyze existing literature on Particle Swarm Optimization (PSO) strategies for target search problems in swarm robotics.
• To provide an in-depth view of current research trends and identify key elements within PSO-based target search.

Main Methods:

• A systematic literature review was conducted following the PRISMA Statement guidelines.
• 51 relevant research studies were identified and analyzed.
• Analysis focused on PSO variants, target search components, and research field elements.

Main Results:

• Nine key elements emerged from the analysis: PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target availability, target mobility status, experiment framework, and environment complexity.
• The review synthesizes current approaches and identifies common themes in PSO for target search.

Conclusions:

• PSO remains a highly relevant and effective algorithm for target search in swarm robotics.
• The identified elements provide a framework for understanding and advancing PSO strategies in this domain.
• Recommendations for future research are presented to guide further development.