Parallel Stochastic Optimization Algorithms

Summary

The project aims to develop solution methodologies for combinatorial problems by using stochastic algorithms.

Supervisor(s)

Professor Albert Y. Zomaya

Research Location

Computer Science

Program Type

Masters/PHD

Synopsis

Optimization algorithms can be used to solve a wide range of problems that arise in the design and operation of parallel computing environments (e.g., data mining, scheduling, routing). However, the many classical optimization techniques (e.g., linear programming) are not suited for solving parallel processing problems due to their restricted nature. This project is investigating the application of some new and unorthodox optimization techniques such fuzzy logic, genetic algorithms, neural networks, simulated annealing, ant colonies, Tabu search, and others. However, these techniques are computationally intensive and require enormous computing time. Parallel processing has the potential of reducing the computational load and enabling the efficient use of these techniques to solve a wide variety of problems.

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Keywords

algorithms, complex systems, Computational Theory, Distributed computing, optimization

Opportunity ID

The opportunity ID for this research opportunity is: 972

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