Solving Complex Real Time Engineering Problems by Artificial Immune System: Case Study of Dynamic Stochastic Knapsack Problem

D. Teodorović, S. Ramaraj, and D. Gračanin (USA)


neural networks, hybrid systems, dynamic programming, dynamic stochastic knapsack problem, Petri nets.


This paper describes an Artificial Immune System (AIS) based approach to modeling phenomenon characterized by uncertainty, and real-time decision-making. The proposed AIS based approach represents a combination of optimization techniques and neural networks. The AIS develops antibodies (the best control strategies) for different antigens (different scenarios). This task is performed using some of the optimization or heuristics techniques to develop an initial set of antibodies. The developed set of antibodies is then combined to create the AIS by constructing an artificial neural network. The proposed approach is applied to the real-time optimization of the dynamic stochastic knapsack problem. A Petri net model of the knapsack is used to generate arrival patterns for different classes of items arriving to the knapsack and to calculate the rewards. Dynamic programming optimization is employed with an objective towards maximizing the reward to perform offline optimization of the knapsack. The obtained optimal values of the input variables (number of items in each class) are used to build and train a multi-layer neural network. The obtained neural network weights and configuration can be used to perform optimal accept/reject decisions in real-time. The preliminary results are very promising.

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