Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary OptimizationReport as inadecuate




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Agent based simulation has successfully been applied to model complex organizational behavior and to improve or optimize aspects of organizational performance. Agents, with intelligence supported through the application of a genetic algorithm are proposed as a means of optimizing the performance of the system being modeled. Local decisions made by agents and other system variables are placed in the genetic encoding. This allows local agents to positively impact high level system performance. A simple, but non trivial, peg game is utilized to introduce the concept. A multiple objective bin packing problem is then solved to demonstrate the potential of the approach in meeting a number of high level goals. The methodology allows not only for a systems level optimization, but also provides data which can be analyzed to determine what constitutes effective agent behavior.

KEYWORDS

Decision Support, Multiple Goal, Agent Based, Genetic Optimization, Bin Packing

Cite this paper

Alobaidi, W. , Sandgren, E. and Alkuam, E. 2017 Decision Support through Intelligent Agent Based Simulation and Multiple Goal Based Evolutionary Optimization. Intelligent Information Management, 9, 97-113. doi: 10.4236-iim.2017.93005.





Author: Wissam Alobaidi1*, Eric Sandgren1*, Entidhar Alkuam2

Source: http://www.scirp.org/



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