Multi co-objective evolutionary optimization: Cross surrogate augmentation for computationally expensive problems

Le, Minh Thuy; Ong, Yew-Soon; Menzel, Stefan; Seah, Chun-Wei; Sendhoff, Bernhard

doi:10.1109/cec.2012.6252915

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…Some existing and recent studies can be applied to expensive MOPs with different latencies among objective functions. For instance, in [86], a transfer learning was used to build surrogate models among correlated objectives. In an extended work in [87], the authors used transfer learning for sharing information between different parts of the Pareto front.…”

Section: A On-line Data-driven Optimizationmentioning

confidence: 99%

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin

Wang

Chugh

et al. 2019

IEEE Trans. Evol. Computat.

465

144

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Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

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Section: A On-line Data-driven Optimizationmentioning

confidence: 99%

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Jin

Wang

Chugh

et al. 2019

IEEE Trans. Evol. Computat.

465

144

View full text Add to dashboard Cite

show abstract

“…More generally, in the context of constrained optimization, two promising research direction may be to learn the structure and location of the feasibility boundary as done, for example, in another study (Handoko, Kwoh, & Ong, ), or extend the idea of cross‐surrogate modelling (Le, Ong, Menzel, Seah, & Sendhoff, ) to constraint functions.…”

Section: Prospective Solutionsmentioning

confidence: 99%

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Allmendinger

Emmerich

Hakanen

et al. 2017

J. Multi-Crit. Decis. Anal.

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Complexity in solving real-world multicriteria optimization problems often stems from the fact that complex, expensive, and/or time-consuming simulation tools or physical experiments are used to evaluate solutions to a problem. In such settings, it is common to use efficient computational models, often known as surrogates or metamodels, to approximate the outcome (objective or constraint function value) of a simulation or physical experiment. The presence of multiple objective functions poses an additional layer of complexity for surrogate-assisted optimization.For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers to approximate efficiently multiple objectives simultaneously. Thinking out of the box, complexity can also be shifted from approximating the individual objective functions to approximating the entire Pareto front. This leads to further complexities, namely, how to validate statistically and apply the techniques developed to real-world problems. In this paper, we discuss emerging complexity-related topics in surrogate-assisted multicriteria optimization that may not be prevalent in nonsurrogate-assisted single-objective optimization. These complexities are motivated using several real-world problems in which the authors were involved. We then discuss several promising future research directions and prospective solutions to tackle emerging complexities in surrogate-assisted multicriteria optimization. Finally, we provide insights from an industrial point of view into how surrogate-assisted multicriteria optimization techniques can be developed and applied within a collaborative business environment to tackle real-world problems.

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“…This was expected due to the heavy computational load of high dimensional hypervolumes and SPEA2 fitness assignments for large populations. Therefore, the 12-objective problem was only evaluated using NSGAII and MOEA/D algorithms with Inverse Generational Distance (IGD) [13] replacing the hypervolume performance indicator.…”

Section: Quality Indicatorsmentioning

confidence: 99%

Improving many-objective optimization performance by sequencing evolutionary algorithms

Dohr

Eichberger

2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Evolutionary multiobjective optimization (EMO) has been successfully applied to various real-world scenarios with usually two or three contradicting optimization goals. However, several studies have pointed out a great deterioration of computational performance when handling more than three objectives. In order to improve the scalability of multiobjective evolutionary algorithms (MOEAs) onto higher-dimensional objective spaces, techniques using e.g. scalarizing functions and preference-or indicator-based guidance have been proposed. Most of those proposals require a-priori information or a decision maker during optimization, which increases the complexity of the algorithms.In this paper, we propose a divide and conquer method for many-objective optimization. First, we partition a problem into lower-dimensional subproblems for which standard algorithms are known to perform very well. Our key improvement is the sequential usage of MOEAs, utilizing the results of one suboptimization as initial population for another MOEA. This technique allows modular optimization phases and can be applied to common evolutionary algorithms. We test our enhanced method on the hard to solve multiobjective Quadratic Assignment Problem (mQAP), using a variety of established MOEAs.

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Multi co-objective evolutionary optimization: Cross surrogate augmentation for computationally expensive problems

Cited by 14 publications

References 23 publications

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Data-Driven Evolutionary Optimization: An Overview and Case Studies

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Improving many-objective optimization performance by sequencing evolutionary algorithms

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