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Parallel Multiagent Coordination Optimization Algorithm: Implementation, Evaluation, and Applications
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DOI：10.1109/TASE.2016.2544749.png)
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Abstract
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In this paper, a parallel computing implementation of a multiagent coordination optimization (MCO) algorithm is introduced using parfor, a built-in MATLAB function. As a novel variation of particle swarm optimization (PSO), the MCO algorithm has demonstrated significant performance improvement, in terms of both accuracy and efficiency, in solving real-time optimization problems when compared with PSO. However, the ability to handle large-scale optimization problems or use more particles in the algorithm is limited by the sequential implementation of the original MCO algorithm. A numerical evaluation of the parallel MCO algorithm was conducted using the supercomputers in the High Performance Computing Center at Texas Tech University. Based on the results of this evaluation, it was determined that the performance of the parallel MCO is not only superior to that of PSO but is highly efficient as it reduces the computational time. The parallel binary MCO (BMCO) algorithm is presented here as well. By combining the parallel MCO with parallel BMCO algorithms, a new parallel mixed-binary nonlinear programming MCO solver is proposed. Finally, a load balancing coordination problem, a multiagent formation control problem, and a power system vulnerability analysis problem are solved using the corresponding parallel MCO algorithm. Note to Practitioners-Optimization-based techniques are viewed as a great success in industrial and engineering applications. With the rapid growth of requirements for optimization algorithms, such as faster convergence and higher accuracy, heuristic optimization algorithms are attracting more and more attention. The proposed multiagent coordination optimization algorithm is a novel heuristic optimization algorithm created by merging multiagent coordination and swarm intelligence together to accelerate the search for the optimal solution. Moreover, the parallelization technique described in this paper greatly reduces the computational time of the algorithm for large-scale optimization problems. This significantly benefits the demand for faster convergence and higher accuracy when solving complex, large-scale industrial problems, such as load balancing coordination, multiagent formation control, and power system vulnerability analysis.
Keywords:
Convergence
load balancing
numerical optimization
parallel computing
semistability
swarm intelligence
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