• Corpus ID: 232290443

PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems

@article{Cuervo2021PAMELIAM,
  title={PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems},
  author={Santiago Cuervo and Miguel A. Melgarejo and Angie Blanco-Canon and Laura Reyes Fajardo and Sergio Rojas Galeano},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.10736}
}
—We present an algorithm for multi-objective opti- mization of computationally expensive problems. The proposed algorithm is based on solving a set of surrogate problems defined by models of the real one, so that only solutions estimated to be approximately Pareto-optimal are evaluated using the real expen- sive functions. Aside of the search for solutions, our algorithm also performs a meta-search for optimal surrogate models and navigation strategies for the optimization landscape, therefore… 

References

SHOWING 1-10 OF 53 REFERENCES

ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems

Results show that NSGA-II, a popular multiobjective evolutionary algorithm, performs well compared with random search, even within the restricted number of evaluations used.

A study of surrogate models for their use in multiobjective evolutionary algorithms

Four meta-modeling techniques are compared in different aspects such as accuracy, robustness, efficiency, and scalability with the aim to identify advantages and drawbacks of each meta- modeling technique in order to choose the most suitable one to be combined with multiobjective evolutionary algorithms.

A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

A surrogate-assisted reference vector guided evolutionary algorithm (SAEA) for computationally expensive optimization problems with more than three objectives that uses Kriging to approximate each objective function to reduce the computational cost.

Parameter Meta-optimization of Metaheuristic Optimization Algorithms

How the optimization of parameters can be automated by using another optimization algorithm on a meta-level by being implemented for the open source optimization environment HeuristicLab.

A Multiple Surrogate Assisted Decomposition-Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization

This paper presents an approach called hybrid surrogate-assisted many-objective evolutionary algorithm to solve computationally expensive MaOPs, which includes the use of multiple surrogates to effectively approximate a wide range of objective functions.

A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

A survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems and identifies and discusses some promising elements and major issues among algorithms in the Literature related to using an approximation and numerical settings used.

Multiple surrogate assisted multiobjective optimization using improved pre-selection

An attempt to exploit the best features of several strategies, and in particular compares two possible versions of pre-selection in multiobjective optimization, based on the non-dominated sorting genetic algorithm NSGA-II.

A Meta-Optimization Approach for Covering Problems in Facility Location

The experimental results show the effectiveness of the meta-optimization approach which produces very near optimal scores when solving set covering instances from the OR-Library.

Calculating Complete and Exact Pareto Front for Multiobjective Optimization: A New Deterministic Approach for Discrete Problems

A new deterministic approach capable of fully determining the real Pareto front for those discrete problems for which it is possible to construct optimization algorithms to find the k best solutions to each of the single-objective problems is proposed.

What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

It is shown that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications.
...