Framework for minimising the impact of regional shocks on global food security using multi-objective ant colony optimisation Peter Golding a, * , Sam Kapadia a , Stella Naylor a , Jonathan Schulz a , Holger R. Maier a , Upmanu Lall b , Marijn van der Velde c a School of Civil, Environmental and Mining Engineering, The University of Adelaide, SA 5005, Australia b Department of Earth and Environmental Engineering, Columbia University, New York, NY, USA c European Commission, Joint Research Centre, Directorate Sustainable Resources, Via E. Fermi 2749, I-21027 Ispra, VA, Italy article info Article history: Received 29 November 2016 Received in revised form 4 April 2017 Accepted 15 June 2017 Keywords: Optimization Global food security Shock mitigation Trade Ant colony optimization Search space size reduction abstract A general framework for the identification of optimal strategies for mitigating the impact of regional shocks to the global food production network is introduced. The framework utilises multi-objective ant colony optimisation (ACO) as the optimisation engine and is applicable to production-, demand-, storage- and distribution-focussed mitigation options. A detailed formulation for using trade as the mitigation option is presented and applied to a shock to wheat production in North America for illustrative pur- poses. Different strategies for improving the performance of the ACO algorithm are also presented and tested. Results indicate that the proposed framework has the potential to identify a range of practical trade mitigation strategies for consideration by decision makers, including trade-offs between the extent to which regional shocks can be mitigated and the degree to which existing trade arrangements have to be modified, as well as the relative importance of various trade agreements and different exporting countries. © 2017 Elsevier Ltd. All rights reserved. Software availability Name of Software: FORTS-ACO Developers: Jonathan Schulz, Peter Golding, Sam Kapadia, Stella Naylor Hardware required: PC or Mac Program language: FORTRAN Program size: 7.85 MB Contact Address: School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia Contact E-mail: peter.golding@hotmail.com Source Code: https://github.com/petergolding/FORTS-ACO Cost: Free for non-commercial use 1. Introduction In an increasingly globalised and technologically advanced world, it is deeply concerning that between 2012 and 2014, 805 million people were chronically undernourished (FAO, 2014). While there have been increasing efforts to address the issue of food se- curity, the complex nature of food production, supply and demand has limited the flow of effective information to decision-makers, inhibiting the development of effective policies and procedures that could allow all humans access to their fundamental rights (FAO, 2014). Much of this complexity stems from the dynamic nature of food security drivers, including population growth, climatic influences, and shocks to the system. In the first half of this century, the world's population is likely to grow to around 9.6 billion (United Nations, 2014), resulting in a 70% increase in global food demand alone (FAO, 2014). According to the US National Research Council (2010), supply-demand systems will be strained at the production side, too, with global average temperatures increasing by up to 11.5  F (6.3  C) before 2100. In addition to these long-term food security drivers, short-term, high-impact ‘shocks’ to the global food network, such as natural disasters or war, can have devastating effects on regional crop production (Lecl ere et al., 2014; Puma et al., 2015). Enhancing capacity to mitigate the global impact of regional * Corresponding author. E-mail address: peter.golding@hotmail.com (P. Golding). Contents lists available at ScienceDirect Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft http://dx.doi.org/10.1016/j.envsoft.2017.06.004 1364-8152/© 2017 Elsevier Ltd. All rights reserved. Environmental Modelling & Software 95 (2017) 303e319