A surrogate-assisted evolution strategy for constrained multi-objective optimization

Datta, Rituparna; Regis, Rommel G.

doi:10.1016/j.eswa.2016.03.044

Cited by 94 publications

(34 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The surrogate model method has been properly utilized to simplify practical models in design problems [33,34]. A surrogateassisted evolution strategy was proposed and applied to multiobjective optimization [35]. Peng and Wang [36] established an effective adaptive surrogate for solving transfer trajectory optimization, the computing speed of which was almost 8 times faster than that of directly solving the actual model.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

Fan

Yoshino

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

An effective hybrid algorithm is proposed for solving multiobjective optimization engineering problems with inequality constraints. The weighted sum technique and BFGS quasi-Newton's method are combined to determine a descent search direction for solving multiobjective optimization problems. To improve the computational efficiency and maintain rapid convergence, a cautious BFGS iterative format is utilized to approximate the Hessian matrices of the objective functions instead of evaluating them exactly. The effectiveness of the proposed algorithm is demonstrated through a comparison study, which is based on numerical examples. Meanwhile, we propose an effective multiobjective optimization strategy based on the algorithm in conjunction with the surrogate model method. This proposed strategy has been applied to the crashworthiness design of the primary energy absorption device's crash box structure and front rail under low-speed frontal collision. The optimal results demonstrate that the proposed methodology is promising in solving multiobjective optimization problems in engineering practice.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

Fan

Yoshino

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…With restrictions that vary and some objective functions in solving optimization problems become the distinct advantage for evolutionary algorithms (Datta and Regis, 2016, Yoosefelahi et al, 2012, Andersen and Santos, 2012. As one of the evolutionary algorithms that implement processes of recombination and mutation, evolution strategies have proven to provide quality solutions to the optimization problem refers to several studies that already exist (Munawaroh and Mahmudy, 2015, Milah and Mahmudy, 2015, Vista and Mahmudy, 2015.…”

Section: Introductionmentioning

confidence: 99%

“…Through a process of imitation survival of living beings of natural selection makes evolutionary algorithms as a promising alternative (Datta and Regis, 2016). With restrictions that vary and some objective functions in solving optimization problems become the distinct advantage for evolutionary algorithms (Datta and Regis, 2016, Yoosefelahi et al, 2012, Andersen and Santos, 2012.…”

Section: Introductionmentioning

confidence: 99%

Profit Optimization Based on Total Production in Textile Home Industry Using Evolution Strategies Algorithms

Khuluqi

Mahmudy

Rahmi

2016

IJLEG

View full text Add to dashboard Cite

Profit optimization became one of the main goals of the production process of home industry. A maximum profit can be achieved with proper planning of production. In the process of implementation, production planning has many constraints such as lot sizing, limited stock, overtime work and the many products derived from the same source. To address the problem, we develop computer software using a heuristic method called evolution strategy (ES). ES has capability to solve optimization problems with nearly optimal results. The result of the calculation process of evolution strategy algorithm was compared with data from the source. Computational analysis shows that ES produces a production plan that has profit of Rp 5,324,000. It is bigger than manual production plan that has profit of Rp 2,747,000.

show abstract

“…We use RBF surrogate models to approximate the objective functions. In contrast to Datta and Regis (2016), our algorithm is not an evolutionary strategy that has been augmented with surrogate models to render the evolutionary strategy applicable to computationally expensive problems. The selection of new sample points is purely based on mutating the parents in the current generation.…”

Section: Introductionmentioning

confidence: 99%

“…Ong et al (2003) developed a parallel evolutionary algorithm that combines surrogate models with a trust region approach. Similarly, Akhtar and Shoemaker (2016) Datta and Regis (2016) introduced SMES-RBF, which is a surrogate-assisted evolutionary strategy for constrained multiobjective optimization problems with black-box objective and constraint functions. Messac and Mullur (2008) introduced a computationally efficient algorithm that uses surrogate models and that is not based on evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems

Müller

2017

INFORMS Journal on Computing

View full text Add to dashboard Cite

We present the algorithm SOCEMO for optimization problems that have multiple conflicting computationally expensive black-box objective functions. The computational expense arising from the objective function evaluations considerably restricts the number of evaluations that can be done to find Pareto-optimal solutions. Frequently used multiobjective optimization methods are based on evolutionary strategies and generally require a prohibitively large number of function evaluations to find a good approximation of the Pareto front. SOCEMO, in contrast, employs surrogate models to approximate the expensive objective functions. These surrogate models are used in the iterative sampling process to decide at which points in the variable domain the next expensive evaluations should be done. Therefore, fewer expensive objective function evaluations are needed, and a good approximation of the Pareto front can be found efficiently. Previous algorithms have generally been tested on problems with few variables (up to 10) and few objective functions (up to 5). In our numerical study, we show that our algorithm performs well for benchmark problems with up to 35 dimensions and up to 10 objective functions, as well as two engineering application problems. We compared the performance of SOCEMO to a variant of NSGA-II and show that SOCEMO’s sophisticated search strategy is more efficient than NSGA-II when the number of allowable function evaluations is low. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0749 .

show abstract

A surrogate-assisted evolution strategy for constrained multi-objective optimization

Cited by 94 publications

References 39 publications

A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

Profit Optimization Based on Total Production in Textile Home Industry Using Evolution Strategies Algorithms

SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems

Contact Info

Product

Resources

About