Introductory overview: Optimization using evolutionary algorithms and other metaheuristics

Abstract

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 tradeoffs 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.

Representation and improved parameterization of reservoir operation in hydrological and land-surface models

“…The most primitive methods use formulations developed for the simulation of natural lakes or uncontrolled reservoirs. In these methods, the downstream release is calculated as a function of reservoir storage characterized by some empirical parameters (Meigh et al, 1999;Döll et al, 2003;Pietroniro et al, 2007;Rost et al, 2008). For instance, Meigh et al (1999) calculate the release by Q t = S 1.5 t , where Q t and S t are release and reservoir storage, respectively.…”

Real-Time, Smart Rainwater Storage Systems: Potential Solution to Mitigate Urban Flooding

“…In order to solve the optimization problem outlined in Section 2.2, a simulation-optimization approach is used [30,31] (Figure 3). An evolutionary algorithm [30] is used to select values of the decision variables (i.e., the combination of the degree of opening of the orifices of each tank at each time step) (see Section 2.2).…”

“…This process of selecting a particular control strategy with the aid of the evolutionary algorithm, evaluating the effectiveness of this strategy using a simulation model, adjusting the control strategy based on the relative success of the previous strategies using the evolutionary algorithm, etc. is repeated hundreds or thousands of times until certain stopping criteria have been met, such as completing a fixed number of iterations or until there has been no reduction in peak flows for a certain number of iterations [31]. Figure 3.…”

Review of Song et al

“…Printer-friendly version Discussion paper 5. When introducing MOEAs, it would be good to cite Maier et al (2019), which is an introductory overview appropriate for readers to be introduced to the topic.…”