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Solving multiconstraint assignment problems using learning automata.

Geir Horn¹, B John Oommen

¹Simula Research Laboratory, 1325 Lysaker, Norway. Geir.Horn@sintef.no

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|November 4, 2009

Summary

This summary is machine-generated.

This study introduces novel learning automata algorithms for the NP-hard object assignment problem, particularly for parallel computing process distribution. These algorithms effectively handle multiple, potentially contradictory constraints, advancing combinatorial optimization solutions.

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Area of Science:

Computer Science
Artificial Intelligence
Operations Research

Background:

The object assignment problem, especially for parallel computing, involves complex multi-constraint optimization.
Existing solutions often use single similarity indices, inadequate for contradictory constraints.
The static mapping problem is a key application in parallel, grid, and cloud computing.

Purpose of the Study:

To address the NP-hard object assignment problem with multiple, potentially contradictory constraints.
To develop and evaluate learning automata (LA)-based algorithms for this problem.
To pioneer LA solutions for complex combinatorial and integer optimization tasks.

Main Methods:

Developed four learning automata (LA)-based algorithms.
Introduced a fixed-structure stochastic automata algorithm with centralized coordination.
Presented three variable-structure stochastic automata (VSSA) algorithms, including process-to-node mapping and communication digraph estimation.

Main Results:

The fixed-structure LA algorithm successfully solves the problem but requires coordination.
VSSA algorithms offer superior partitioning in specific settings, though with some scalability trade-offs.
Demonstrated LA's capability in solving complex combinatorial and integer optimization problems.

Conclusions:

Learning automata provide a viable heuristic approach for multi-constraint object assignment problems.
The developed LA algorithms, particularly VSSA, offer effective solutions for process distribution in parallel computing.
This work represents pioneering LA solutions for complex optimization challenges.