Parallel Stochastic Optimization Algorithms

Summary

This project is investigating the application of unorthodox optimization techniques such fuzzy logic, genetic algorithms, neural networks, simulated annealing, ant colonies, Tabu search, and others to develop optimal interventions at the cellular body social and environmental level towards sustainable health.

Supervisor(s)

Professor Albert Y. Zomaya, Professor John Crawford, Professor Liaquat Hossain, Professor Mathew Vadas

Research Location

Charles Perkins Centre – the Judith and David Coffey Life Lab

Program Type

Masters/PHD

Synopsis

Optimization algorithms can be used to solve a wide range of problems that arise in the environment-food-health system (e.g., policy and economic intervention strategies, environmental regeneration through use). 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 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 architectures (GPUs, FPGAs, Multicores, Clusters, etc) have the potential for reducing the computational load and enabling the efficient use of these techniques to solve a wide variety of problems.

Additional Information

The Life Lab creates a new kind of graduate and postgraduate training environment at the interface between life, social, economic and physical sciences. Its focus is to address the significant challenges we face from an unsustainable food system that degrades the environmental services it depends on, and creates significant societal health problems. A better understanding of the complexity of the environment-food-health nexus is critical. It is fundamental to building a sustainable society, and one that is more robust to future uncertainties. Our unique approach will be a world-first in shifting research on these growing challenges from treating symptoms to prevention.Life Lab will challenge existing paradigms and university models to create a research training environment in which traditional disciplinary boundaries do not apply. Our ambitious vision is to create an ‘innovation hub' where researchers from disciplines spanning physical, life and social and economic sciences will interface with business, government and agency leaders. It will develop integrated approaches to the challenges that threaten societal wellbeing, and train the next generation of experts with the skills required to find solutions.  

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Keywords

Fuzzy logic, optimization, neural networks, health, genetic algorithms, Environment

Opportunity ID

The opportunity ID for this research opportunity is: 1684

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