Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.
1.Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA 5005, Australia 2.Univ Saskatchewan, Global Inst Water Secur, Saskatoon, SK, Canada 3.Univ Saskatchewan, Sch Environm & Sustainabil, Saskatoon, SK, Canada 4.Univ Saskatchewan, Dept Civil & Geol Engn, Saskatoon, SK, Canada 5.Univ Exeter, Coll Engn Math & Phys Sci, Harrison Bldg,North Pk Rd, Exeter EX4 4QF, Devon, England 6.SUNY Buffalo, Ctr Computat Res, Buffalo, NY 14203 USA 7.Univ Colorado, Civil Environm & Architectural Engn Dept, UCB 428, Boulder, CO 80309 USA 8.Univ Waterloo, Dept Civil & Environm Engn, Waterloo, ON N2L 3G1, Canada 9.Delft Univ Technol, Fac Civil Engn & Geosci, Dept Water Management, Stevinweg 1, NL-2628 CN Delft, Netherlands
Recommended Citation:
Maier, H. R.,Razavi, S.,Kapelan, Z.,et al. Introductory overview: Optimization using evolutionary algorithms and other metaheuristics[J]. ENVIRONMENTAL MODELLING & SOFTWARE,2019-01-01,114:195-213