(246t) Multi-Agent Optimization Framework (MAOP) for Large Scale Process System Engineering Optimization Problems

Multi-agent
Optimization Framework (MAOP) for Large Scale Process System Engineering Optimization
Problems

Berhane
H. Gebreslassie and Urmila M. Diwekar

Center
for Uncertain Systems, Tools for Optimization and Management
(CUSTOM):Vishwamitra Research Institute, Crystal Lake, IL 60012 ? USA

berhane@vri-custom.org; urmila@vri-custom.org

Abstract

The multi-agent
optimization (MAOP) framework provides a way of combining various algorithms in
one platform and exploits the strength possessed by each algorithm. Most of large
scale process system engineering problems include nonlinear and non-convex
problems. Contrary to the standalone optimization algorithm framework, the MAOP
strategy avoids the problem of getting stuck in local optima as well as improving
the computational efficiency.  In this work, we propose a multi-agent
optimization framework for solving complex large scale process system
engineering problems. The framework uses a variety of different algorithmic
agents which include the gradient based local optimizers and metaheuristic
algorithms (efficient simulated annealing, efficient genetic algorithm and
efficient ant colony optimization algorithms). Each
agent encapsulates a particular problem-solving procedure. We investigate the effect
of cooperation among agents of the multi-agent system working in parallel and
combined into a framework designed to solve large scale combinatorial
optimization problems. Computational experiments are carried out using benchmark
problems and real world case study. The proposed methodology enables to improve
the quality of solutions and the computational efficiency in comparison with non-cooperative
multi-agent framework and standalone agents. We have also examined solving the
optimization problems with cooperative homogenous and heterogeneous agent
systems and the results indicate that the computational efficiencies are
improved when the agents are heterogeneous. Moreover, the analysis of the intra-
and inter agent cooperation shows that depending on the complexity of
the problem the inter- and intra-agent collaboration has a significant impact
on system performance. The MAOP framework which includes the five major parts
of the algorithm (figure 1) is given in the figure 2. 

Fig. 1. The major parts
of the MAOP algorithm and the information flow direction.

Fig. 2. The basic
flow diagram of the heterogeneous MAOP algorithm

References

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Siirola
JD, Steinar Hauan S, and Westerberg AW, 2003. Toward agent-based process systems engineering:
proposed framework and application to non-convex optimization. Computers and Chemical Engineering 27: 1801-1811.

2.     
Gebreslassie
BH, and Diwekar UM, 2015. Efficient ant colony optimization for computer aided
molecular design: Case study solvent selection problem. Computers and
Chemical Engineering 78: 1-9.

3.     
Diwekar UM,
Xu W, 2005. Improved genetic algorithms for deterministic optimization and
optimization under uncertainty. part i. algorithms development. Industrial
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