A computational intelligence algorithm for expensive engineering optimization problems

Tenne, Yoel

doi:10.1016/j.engappai.2012.03.009

Cited by 45 publications

(25 citation statements)

References 20 publications

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“…There are a number of reviews of the use of these techniques in optimisation, including Jin (2005) and Knowles and Nakayama (2008), as well as a dedicated edited volume on "Computational Intelligence in Expensive Optimization Problems" (Tenne and Goh, 2010) (in particular, reviews by Shi and Rasheed (2010) and Santana-Quintero et al (2010) therein). In addition, Razavi et al (2012) presented an extensive review of surrogate modelling in water resources and recent developments and applications to environmental systems are also presented in a special issue on "Emulation techniques for the reduction and sensitivity analysis of complex environmental models" (see Ratto et al, 2012).…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

View full text Add to dashboard Cite

show abstract

“…In real-life applications it is common to find objective functions and constraints that are computationally expensive to evaluate [50,51]. In these cases, it is required to build an approximation model to assess solutions employing polynomial regression [52], neural networks [53][54][55], SVM [56], Markov fitness models [57], kriging [58] or radial basis functions [59], for example.…”

Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

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This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

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“…Such optimization problems are commonly referred to in the literature as expensive black-box optimization problems, and a wide range of algorithms has been proposed in an attempt to effectively handle them (Tenne and Goh, 2010;Regis and Shoemaker, 2013;Muller and Shoemaker, 2014). On top of the above challenges, such problems often present an additional difficulty which is related to the evaluation process: for some candidate designs the simulation will fail, and no objective value will be provided.…”

Section: Introductionmentioning

confidence: 99%

“…This allows us to investigate scenarios which may be complicated to evaluate by real-world experiments, and reduces the costs associated with product development. Such computer simulations, which must be properly validated with laboratory experiments, transform the design process into an optimization problem which is characterized by the following aspects (Tenne and Goh, 2010):…”

Section: Introductionmentioning

confidence: 99%

Machine–Learning in Optimization of Expensive Black–Box Functions

Tenne

2017

International Journal of Applied Mathematics and Computer Science

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Modern engineering design optimization often uses computer simulations to evaluate candidate designs. For some of these designs the simulation can fail for an unknown reason, which in turn may hamper the optimization process. To handle such scenarios more effectively, this study proposes the integration of classifiers, borrowed from the domain of machine learning, into the optimization process. Several implementations of the proposed approach are described. An extensive set of numerical experiments shows that the proposed approach improves search effectiveness.

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A computational intelligence algorithm for expensive engineering optimization problems

Cited by 45 publications

References 20 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Machine–Learning in Optimization of Expensive Black–Box Functions

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