Abstract
In this paper, a novel variation of Particle Swarm Optimization (PSO) algorithm, called Multiagent Coordination Optimization (MCO), is implemented in a parallel computing way for practical use by introducing MATLAB built-in function "parfor" into MCO. Then we present the global convergence result for MCO. Besides sharing global optimal solutions with the PSO algorithm, the MCO algorithm integrates cooperative swarm behavior of multiple agents into the update formula by sharing velocity and position information between neighbors to improve its performance. Numerical evaluation of the parallel MCO algorithm is provided in the paper by running the proposed algorithm on supercomputers. Finally, as an application, balanced coordination for damage mitigation and resource allocation in network systems is studied and solved by parallel MCO, serial MCO, and PSO.
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