Multi-objective Optimization and GIS to Improve Climate Change Induced Disaster Risk Management in Africa

We live in an era that suffers from climate change issues, not least disaster risks induced by climate change. While measures, such as proper evacuation plans, are required to reduce the negative impacts of disasters when they hit, actions should also be taken to reduce climate change effects by e.g. increasing the use of renewable energies or proper urban land-use allocation, where climate change factors are considered among other criteria. Planning and decision-making on these issues are usually complex and complicated, since several criteria, usually conflicting with each other, should be taken into consideration.

Multi-objective optimization (MOO) has proven to be a proper technique for solving multi-criteria decision analysis problems, where criteria are conflicting. Metaheuristic algorithms, inspired from nature, has been developing and showing a proper performance for solving complex MOO problems. Meanwhile, these algorithms yet need to be modified and adjusted to perform well, for each specific case study project. This research aims to improve metaheuristic algorithms to make them suitable for solving some spatial problems related to disaster risk management.