环境建模与软件--毕业设计外文翻译.doc
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外文翻译
Environmental Modelling&Software
1.1. Background
Environmental change, economic and social pressures, and limited resources
motivate systems analysis techniques that can help planners determine new
management strategies, develop better designs and operational regimes, improve and
calibrate simulation models, and resolve conflicts between divergent stakeholders.
Metaheuristics are emerging as popular tools to facilitate these tasks, and in the field
of water resources, they have been used extensively for a variety of purposes (e.g.
model calibration, the planning, design and operation of water resources systems etc.)
in many different application areas over the last few decades (Nicklow et al., 2010).
Since metaheuristics were first applied in the water resources field (Dougherty and
Marryott,1991; McKinney and Lin,1994; Ritzel et al.,1994; Gupta et al.,1998), their
popularity has increased dramatically, probably facilitated by the simultaneous
increase of available computational power (Washington et al., 2009), to the point
where they are widely used (Nicklow et al., 2010), even by actual water planning
utilities (Basdekas, 2014).
Zufferey (2012) defines a metaheuristic “as an iterative generation process which
guides a subordinate heuristic by combining intelligently different concepts for
exploring and exploiting the search space”, as part of which “learning strategies are
used to structure information in order to find efficiently near-optimal solutions.”
Unlike more “traditional” approaches, which use mathematical programming to
specify the optimal value of one or more objective functions, metaheuristics
incorporate elements of structured randomness for search and follow empirical
guidelines, often motivated by observations of natural phenomena (Collette and Siarry,
2003).
Metaheuristics can be divided into two groups, including population-based
algorithms (e.g. genetic algorithms, evolutionary strategies, particle swarm
optimization, ant colony optimization, etc.) and single point-based methods (e.g.
simulated annealing, tabu search, simple (1+1) evolutionary strategies, trajectoryor
local search methods, etc.). Evolutionary algorithms (EAs) are the most
well-established class of metaheuristics for solving water resources problems and are
inspired by various mechanisms of biological evolution (e.g. reproduction, mutation,
crossover, selection, etc.) (Nicklow et al., 2010). Consequently, the focus of the
remainder of this paper is on EAs, although many of the concepts discussed also
broadly apply to other metaheuristics. The paper also provides general guidelines and
future research directions for the broader class of systems analysis approaches that
take any sort of optimisation into account.
When using EAs, the steps in the optimisation process generally include:
1.Problem formulation (i.e. selection and definition of decision variables,
objectives, and constraints).
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