A binary cooperative bat algorithm based optimal topology design of leader-follower consensus

Area of Science:

• Optimization Algorithms
• Multi-Agent Systems
• Control Theory

Background:

• Binary optimization problems present unique challenges.
• Cooperative bat searching algorithm (CBA) is an extension of the bat searching algorithm.
• Leader-follower consensus is crucial for coordinated multi-agent system behavior.

Purpose of the Study:

• To develop a novel binary cooperative bat searching algorithm (BCBA) for binary optimization.
• To investigate the performance of BCBA in solving the leader-follower consensus problem.
• To explore optimal communication topologies for multi-agent systems.

Main Methods:

• The binary cooperative bat searching algorithm (BCBA) is developed by adapting the CBA with a consensus term.
• Velocity values are mapped to probabilities for binary position updates.
• Four different mapping functions are analyzed to identify the optimal transfer function.
• BCBA is applied to a binary variable optimal topology design problem for leader-follower consensus.

Main Results:

• BCBA demonstrates superior performance in binary optimization tasks compared to four existing binary algorithms.
• The study relaxes communication topology conditions for leader-follower protocols.
• Numerical evaluations confirm the competitive performance of BCBA in addressing the optimal topology design problem.

Conclusions:

• BCBA is an effective algorithm for binary optimization problems.
• The developed BCBA can successfully address the leader-follower consensus problem with relaxed communication topology constraints.
• The algorithm offers a viable approach for optimizing the trade-off between convergence rate and communication cost in multi-agent systems.