Convergence Analysis and Parallel Computing Implementation for the Multiagent Coordination Optimization Algorithm
This work addresses optimization challenges in nonlinear, nonconvex problems for researchers and practitioners, but it is incremental as it builds on existing PSO methods.
The authors tackled the problem of improving Particle Swarm Optimization by developing a Multiagent Coordination Optimization algorithm, implementing it in parallel computing, and rigorously analyzing its convergence, resulting in superior performance and time savings compared to PSO and serial MCO on benchmark functions.
In this report, 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 rigorously analyze the global convergence of MCO by means of semistability theory. 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 report by running the proposed algorithm on supercomputers in the High Performance Computing Center at Texas Tech University. In particular, the optimal value and consuming time are compared with PSO and serial MCO by solving several benchmark functions in the literature, respectively. Based on the simulation results, the performance of the parallel MCO is not only superb compared with PSO for solving many nonlinear, noncovex optimization problems, but also is of high efficiency by saving the computational time.