Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling

Li, G.; Li, M.; Azarm, Shapour; Hashimi, Saleh Al; Ameri, T. Al; Qasas, N. Al

doi:10.1007/s00158-008-0251-6

Cited by 32 publications

(8 citation statements)

References 26 publications

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“…The same algorithm was extended to KD-MOGA in [81] with one additional element. In KD-MOGA, a fixed number of individuals is generated using constrained maximum entropy design after step 9, which is an extension of unconstrained maximum entropy design [25].…”

Section: Kriging Based Algorithmsmentioning

confidence: 99%

“…The number of papers which used benchmark and real-world problems is also mentioned in Figure 3. Seven algorithms [84,104,107,110,127,131,148] were tested on benchmark problems, seven algorithms [6,48,52,59,75,94,128] on real-world problems and seven algorithms [14,81,83,85,86,90,151,18] on both. The efficiency of the different algorithms in terms of computation time or number of function evaluations reduced is very important, especially in the case of real-world problems.…”

Section: Comparison Of Function Approximation Based Algorithmsmentioning

confidence: 99%

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

et al. 2017

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Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008-2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation based algorithms. We also compare these algorithms based on different criteria such as metamodelling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive mmultiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available.

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Section: Kriging Based Algorithmsmentioning

confidence: 99%

Section: Comparison Of Function Approximation Based Algorithmsmentioning

confidence: 99%

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

et al. 2017

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“…Concerning MMOGA algorithm, the output responses are improved, adjusted with higher level variables, and presented in two-dimensional forms. Kriging algorithm [21][22][23][24]] is a precise multi-dimensional interpolation using a simple polynomial function. Its efficiency can be maximized based on the capability of estimating the errors and modifying the preliminary population.…”

Section: Moga and Mmogamentioning

confidence: 99%

Detecting structural damage in Timoshenko beams based on optimization via simulation (OVS)

Farokhzad¹,

Mohebi²,

Amiri³

et al. 2016

J. vibroeng.

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Abstract. Considering easy determination of natural frequency in structures leads researchers to focus on detecting the damage through the dynamic parameters using combination of various artificial intelligence algorithms. The main contribution of this research is to detect damage in structures (including its depth and location) for the deep beams with Timoshenko behavior using optimization via simulation (OVS). This method is established based on the first three natural frequencies of the deep or semi-deep beams. The finite element method (FEM) is conducted to obtain essential inputs parameters for OVS. The exact location and depth of the structural damage are determined, using combination of multi-objective optimization algorithms, multi-objective genetic algorithm (MOGA), and modified multi-objective genetic algorithm (MMOGA). This research also remarkably concerns about detecting the location of the defect in the beams with several cracks. In order to verify the results obtained from numerical analysis, several experimental specimens are presented. The dynamics parameters of the beams are experimentally identified using modal hammer. The responses obtained from the numerical method, proposed in this research, are also compared with the results obtained from previous studies. Practically, a beam with real dimensions is examined for different boundary conditions. In addition, the results obtained from MOGA and MMOGA are compared with the other researchers' achievements. Finally, it was observed that the proposed method, (OVS), can be satisfyingly determined the exact location and depth of damage with the high accuracy.Keywords: first three natural frequency, multi-objective genetic algorithm (MOGA), modified multi-objective genetic algorithm (MMOGA), optimization via simulation (OVS), and Timoshenko beam.

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“…Metamodel assisted fitness evaluation procedures and adaptive design of experiments have also been successfully combined with a multi-objective genetic algorithm (Li et al, 2009). …”

Section: Adaptive Metamodelingmentioning

confidence: 99%

Multi-Objective Reliability-Based Optimization with Stochastic Metamodels

Coelho

Bouillard

2011

Evolutionary Computation

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This paper addresses continuous optimization problems with multiple objectives and parameter uncertainty defined by probability distributions. First, a reliability-based formulation is proposed, defining the nondeterministic Pareto set as the minimal solutions such that user-defined probabilities of nondominance and constraint satisfaction are guaranteed. The formulation can be incorporated with minor modifications in a multiobjective evolutionary algorithm (here: the nondominated sorting genetic algorithm-II). Then, in the perspective of applying the method to large-scale structural engineering problems—for which the computational effort devoted to the optimization algorithm itself is negligible in comparison with the simulation—the second part of the study is concerned with the need to reduce the number of function evaluations while avoiding modification of the simulation code. Therefore, nonintrusive stochastic metamodels are developed in two steps. First, for a given sampling of the deterministic variables, a preliminary decomposition of the random responses (objectives and constraints) is performed through polynomial chaos expansion (PCE), allowing a representation of the responses by a limited set of coefficients. Then, a metamodel is carried out by kriging interpolation of the PCE coefficients with respect to the deterministic variables. The method has been tested successfully on seven analytical test cases and on the 10-bar truss benchmark, demonstrating the potential of the proposed approach to provide reliability-based Pareto solutions at a reasonable computational cost.

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Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling

Cited by 32 publications

References 26 publications

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

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

Detecting structural damage in Timoshenko beams based on optimization via simulation (OVS)

Multi-Objective Reliability-Based Optimization with Stochastic Metamodels

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