Noise multi-objective evolutionary algorithms

Program Code: 
1885
Contact: 

Prof Hussein Abbass (h.abbass@adfa.edu.au)

Description of Work: 

Objectives:

Real world problems almost always have multiple objectives that need to be optimized simultaneously despite the conflict that may exist among these objectives. Evolutionary multi-objective optimisation (EMO) is an efficient way to solve these problems. The objective of this project is to develop new EMO algorithms that are able to scale-up to many objectives under high level of noise. The successful manifestation of such algorithms will be tested on large-scale combinatorial realworld optimization problems in domains such as air traffic flow management and planning.

Expected Background Knowledge:

  • Knowledge or demonstrated ability to do programming in parallel highperformance computing environment using C or JAVA
  • Good understanding of Search and Evolutionary Computation

Description of work:

  • A literature review of evolutionary multi-objective optimization algorithms
  • Creating new scalable evolutionary multi-objective optimization algorithms
  • Testing the new algorithms
  • Using the most efficient algorithm in real-world problem solving