Multiobjective Design Optimization by an Evolutionary Algorithm

Ray, Tapabrata; Tai, Kang; Seow, Kin Chye

doi:10.1080/03052150108940926

Cited by 233 publications

(72 citation statements)

References 15 publications

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“…Over the past decade, the development of evolutionary algorithms to solve multi-objective optimization problems has attracted many interest and a number of multi-objective evolutionary algorithms have been suggested (Coello 2006;Schaffer 1985;Fonseca andFleming 1995, 1998;Van Veldhuizen and Lamont 1998;Zitzler and Thiele 1999;Zitzler et al 2000Zitzler et al , 2001Knowles and Corne 2000;Ray et al 2001;Deb et al 2002;Coello et al 2004;Nebro et al 2007). The main reason is that these algorithms are all population-based and their ability to find multiple Paretooptimal solutions in one single run.…”

Section: Introductionmentioning

confidence: 98%

Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

2009

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A self-adaptive differential evolution algorithm incorporate Pareto dominance to solve multi-objective optimization problems is presented. The proposed approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. The experiments were performed using eighteen benchmark test functions. The experiment results show that, compared with three other multi-objective optimization evolutionary algorithms, the proposed MOSADE is able to find better spread of solutions with better convergence to the Pareto front and preserve the diversity of Pareto optimal solutions more efficiently.

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Section: Introductionmentioning

confidence: 98%

Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

2009

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“…Optimization is an activity (which could be single or multiobjective) that aims at finding the best (that is, optimal) solution to a problem. Single-objective optimization is scalar valued with a single unique solution whereas when the objective is vector valued, the optimization process is referred to as multiobjective [Ray et al 2001]. In this delamination detection optimization problem, the objective function is defined as a single objective function and two key components are effectively required to solve the optimization problem:…”

Section: Mathematical Formulationmentioning

confidence: 99%

Efficiencies of algorithms for vibration-based delamination detection: A comparative study

Ihesiulor¹,

Shankar²,

Zhang³

et al. 2013

J. Mech. Mater. Struct.

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The need for efficient and low-cost techniques adequate for damage detection has become of great interest in engineering applications where structural health monitoring (SHM) is of paramount importance. Promising algorithms for SHM have to deliver results with very low computational and response time requirements and be trustworthy within a certain accuracy. Different algorithms (artificial neural networks (ANN), response surface methodology (RSM), and optimization techniques -gradient-based local search (GBLS) and nondominated sorting genetic algorithms (NSGA-II)) are proposed to fill this research gap. The concept of a surrogate model as a fast-executing model is also introduced. Because the objective of this paper is to concentrate on viable techniques suitable for damage detection using vibration methods with very low computational requirements, surrogates are therefore employed to curtail the computational expense. Particularly of interest among the proposed algorithms is RSM, the principle of which has proved successful in the pharmaceuticals industry over the years. However, RSM has not been so widely used in the field of structural engineering for delamination detection. In this paper, we have demonstrated that a fourth-order polynomial has the capability to detect delaminations in composite structures. In order to reduce the size of training data required to solve the inverse problem by the proposed algorithms, the idea of a suitable design space is brought to the limelight as the combination of all possible simulations that one is concerned about. Since the overall sum of design space is usually prohibitively large, we have used K-means clustering to effectively achieve this. This research concerns the application of ANN, RSM, and optimization techniques for delamination detection using changes in natural frequencies before and after damage. Efficiencies of algorithms (ANN, GBLS, and NSGA-II) are compared with the developed RSM models in terms of the accuracy of delamination detection and response time requirements. The methods have been shown to compete effectively for delamination detection and are accurate in detecting the size and locations of delaminations at midplanes. RSM has a unique feature in that it produces models with a small training dataset requirement and also generates mathematical models that are easy to interpret and implement. The optimization techniques, when integrated with surrogate models, require small training sets clustered through the entire design space. ANN, however, requires large training datasets to achieve its results. As such, the potential of these algorithms as tools for on-board damage detection when integrated into a SHM system is successfully demonstrated.

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“…For Pareto optimal solutions with obj objectives and solutions, a membership function denotes the th objective function of a solution in Pareto optimal solutions, which is defined as [27] …”

Section: Fuzzy Set Theorymentioning

confidence: 99%

Multiobjective Optimization of Steering Mechanism for Rotary Steering System Using Modified NSGA-II and Fuzzy Set Theory

Niu

et al. 2015

Mathematical Problems in Engineering

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Due to the complicated design process of gear train, optimization is a significant approach to improve design efficiency. However, the design of gear train is a complex multiobjective optimization with mixed continuous-discrete variables under numerous nonlinear constraints, and conventional optimization algorithms are not suitable to deal with such optimization problems. In this paper, based on the established dynamic model of steering mechanism for rotary steering system, the key component of which is a planetary gear set with teeth number difference, the optimization problem of steering mechanism is formulated to achieve minimum dynamic responses and outer diameter by optimizing structural parameters under geometric, kinematic, and strength constraints. An optimization procedure based on modified NSGA-II by incorporating dynamic crowding distance strategies and fuzzy set theory is applied to the multiobjective optimization. For comparative purpose, NSGA-II is also employed to obtain Pareto optimal set, and dynamic responses of original and optimized designs are compared. The results show the optimized design has better dynamic responses with minimum outer diameter and the response decay decreases faster. The optimization procedure is feasible to the design of gear train, and this study can provide guidance for designer at the preliminary design phase of mechanical structures with gear train.

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Multiobjective Design Optimization by an Evolutionary Algorithm

Cited by 233 publications

References 15 publications

Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

Efficiencies of algorithms for vibration-based delamination detection: A comparative study

Multiobjective Optimization of Steering Mechanism for Rotary Steering System Using Modified NSGA-II and Fuzzy Set Theory

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