Solving Multi-objective Combinatorial Optimization Problems Using Evolutionary Algorithms

The aim of this research is to expand upon the work conducted as part of my MSc where I developed a multi-objective evolutionary algorithm for an assignment problem based on a shcool I worked at previously whereby 160-year 7 students had to be assigned to 5 tutor groups.

The algorithm choices and novel crossover methods applied to this specific assignment problem are to be applied to a wider variety of assignment problems and alternative combinatorial problems.

Therefore, the overall aim is to attempt to solve other combinatorial problems using the same or similar techniques, demonstrating the use of a variety of evolutionary algorithms such as NSGA-II and NSGA-III, comparing different crossover methods and exploring a wider range of parameter values to further improve upon solutions for combinatorial problems.

It would also be ideal to expand the scope to consider and compare against alternative MOEA approaches to find better solutions for common real-world problems.