Towards More Effective Multi-objective Meta-Heuristics to Solve Complex Combinatorial Problems

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This research project proposes the investigation of a number of challenging ideas in the field of multi-objective combinatorial search. Most of the research in this area has been based on recycling knowledge acquired from research on the single-objective case and this has inspired the extension of many single-objective techniques to their multi-objective variants. Many of these are extensions from evolutionary techniques such as genetic algorithms, evolutionary strategies, particle swarm optimisation and others. There are some extensions of other meta-heuristics such as tabu search and simulated annealing to multi-objective variants. Evolutionary approaches have received most of the attention in multi-objective heuristic search but I believe that a wider range of meta-heuristic techniques should be investigated. The research themes proposed here represent a considerable shift in emphasis. Specifically, the aim is to conceive more effective multi-objective meta-heuristics to tackle complex combinatorial problems in a more effective and efficient manner than the current state of the art is capable of. This proposal aims to re-think the design of multi-objective meta-heuristics by developing them within the multi-objective paradigm instead of modifying known single-objective approaches.