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This study introduces an improved particle swarm optimization (PSO) algorithm for multi-agent simulation. The enhanced PSO balances exploration and exploitation, leading to faster convergence and better solutions in complex problems.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Multi-Agent Systems

Background:

  • Existing research combines multi-agent simulation and particle swarm optimization (PSO) but lacks a universal framework.
  • Standard PSO struggles with complex problems due to poor exploration-exploitation balance.
  • Combining multi-agent simulation with improved PSO algorithms is uncommon.

Purpose of the Study:

  • To propose an improved PSO algorithm with a multi-level structure and competition mechanism.
  • To develop a problem-independent simulation-optimization approach integrating enhanced PSO with multi-agent systems.
  • To apply the approach to dynamic simulation and simultaneous optimization of real-world problems.

Main Methods:

  • Developed an enhanced PSO algorithm featuring a multi-level structure and competition mechanism.
  • Integrated the enhanced PSO into a problem-independent simulation-optimization framework for multi-agent systems.
  • Validated the approach through comparative experiments and a case study on oil spill response planning.

Main Results:

  • The enhanced PSO algorithm demonstrated fast convergence to high-quality solutions while maintaining swarm diversity.
  • The simulation-optimization approach effectively simulated dynamic scenarios and solved associated optimization problems.
  • Comparative experiments confirmed the superiority of the proposed method over standard approaches.

Conclusions:

  • The proposed enhanced PSO algorithm and simulation-optimization framework offer a versatile solution for complex problems.
  • The approach significantly improves response time, efficiency, and environmental mitigation in practical applications like disaster response.
  • This work provides a generalizable method for integrating advanced optimization with multi-agent simulation.