Coordinating metaheuristic agents with swarm intelligence

TL;DR

This paper demonstrates that combining swarm intelligence techniques, specifically simulated annealing agents with particle swarm optimization, significantly improves multi-agent coordination in solving complex optimization problems like the multidimensional knapsack problem.

Contribution

It introduces a novel approach of integrating simulated annealing agents with particle swarm optimization for enhanced multi-agent coordination.

Findings

The combined swarm approach outperforms previous methods on the multidimensional knapsack problem.

Swarm intelligence improves coordination efficiency in metaheuristic multi-agent systems.

Experimental results show significant performance gains over existing solutions.

Abstract

Coordination of multi agent systems remains as a problem since there is no prominent method to completely solve this problem. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems with metaheuristic algorithms. The idea borrowed from swarm intelligence seems working much better than those implementations suggested before. This paper reports the performance of swarms of simulated annealing agents collaborating with particle swarm optimization algorithm. The proposed approach is implemented for multidimensional knapsack problem and has resulted much better than some other works published before.