A Flexible Optimization Procedure for Mechanical Component Design Based on Genetic Adaptive Search

Deb, Kalyanmoy; Goyal, Mayank Kumar

doi:10.1115/1.2826954

Cited by 74 publications

(47 citation statements)

References 4 publications

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“…The aim in the optimization of a design is to minimize or maximize a design objective which satisfies the set of a given constraint for the design problem. In engineering design problem the design variables are usually of discrete or continuous type [4,5].…”

Section: Introductionmentioning

confidence: 99%

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Neve¹,

Kakandikar²,

Kulkarni³

2017

Int J Swarm Intel Evol Comput

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Section: Introductionmentioning

confidence: 99%

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Neve¹,

Kakandikar²,

Kulkarni³

2017

Int J Swarm Intel Evol Comput

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“…The third group uses a categorical optimization approach [Abramson, 2002, 2004, Abramson et al, 2009, Deb and Goyal, 1998, Kokkolaras et al, 2001, Ocenasek and Schwarz, 2002] to directly handle discrete variables without a continuous relaxation. Thus, any possible ordering relations that may exist between discrete variables are ignored and, thus, all discrete variables, ordinal and categorical, are treated as categorical ones.…”

Section: Introductionmentioning

confidence: 99%

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Liao¹

2015

4OR-Q J Oper Res

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ii SummaryContinuous optimization problems are optimization problems where all variables have a domain that typically is a subset of the real numbers; mixed discretecontinuous optimization problems have additionally other types of variables, so that some variables are continuous and others are on an ordinal or categorical scale. Continuous and mixed discrete-continuous problems have a wide range of applications in disciplines such as computer science, mechanical or electrical engineering, economics and bioinformatics. These problems are also often hard to solve due to their inherent difficulties such as a large number of variables, many local optima or other factors making problems hard. Therefore, in this thesis our focus is on the design, engineering and configuration of high-performing heuristic optimization algorithms.We tackle continuous and mixed discrete-continuous optimization problems with two classes of population-based heuristic algorithms, ant colony optimization (ACO) algorithms and evolution strategies. In a nutshell, the main contributions of this thesis are that (i) we advance the design and engineering of ACO algorithms to algorithms that are competitive or superior to recent state-of-the-art algorithms for continuous and mixed discrete-continuous optimization problems, (ii) we improve upon a specific state-of-the-art evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), and (iii) we extend CMA-ES to tackle mixed discrete-continuous optimization problems.More in detail, we propose a unified ant colony optimization (ACO) framework for continuous optimization (UACOR). This framework synthesizes algorithmic components of two ACO algorithms that have been proposed in the literature and an incremental ACO algorithm with local search for continuous optimization, which we have proposed during my doctoral research. The design of UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new, high-performing ACO algorithms for continuous optimization. We also propose iCMAES-ILS, a hybrid algorithm that loosely couples IPOP-CMA-ES, a CMA-ES variant that uses a restart schema coupled with an increasing population iii size, and a new iterated local search (ILS) algorithm for continuous optimization. The hybrid algorithm consists of an initial competition phase, in which IPOP-CMA-ES and the ILS algorithm compete for further deployment during a second phase. A cooperative aspect of the hybrid algorithm is implemented in the form of some limited information exchange from IPOP-CMA-ES to the ILS algorithm during the initial phase. Experimental studies on recent benchmark functions suites show that UACOR and iCMAES-ILS are competitive or superior to other state-of-the-art algorithms.To tackle mixed discrete-continuous optimization problems, we extend ACO MV and propose CES MV , an ant colony optimization algorithm and a covariance matrix adaptation evolution strategy, respectively. In ACO MV and CES MV , the decision variables of an optimization prob...

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“…This leads to mixed-integer nonlinear problems, lacking the properties of differentiability and convexity. Deb and Goyal (1998), in the context of mechanical component design, refer to the problems that can emerge when using classical optimization techniques, which are designed to use continuous variables, in dealing with integer variables at the expense of introducing additional constraints.…”

Section: Introductionmentioning

confidence: 99%

Multiple- and single-objective approaches to laminate optimization with genetic algorithms

Costa

Fernandes

Figueiredo

et al. 2004

Structural and Multidisciplinary Optimization

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In this paper an application of a genetic algorithm to a material-and sizing-optimization problem of a plate is described. This approach has obvious advantages: it does not require any derivative information and it does not impose any restriction, in terms of convexity, on the solution space. The plate optimization problem is firstly formulated as a constrained mixed-integer programming problem with a single objective function. An alternative multiobjective formulation of the problem in which some constraints are included as additional objectives is also presented. Some numerical results are included that show the appropriateness of the algorithm and of the mathematical model for the solution of this optimization problem, as well as the superiority of the multiobjective approach.

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A Flexible Optimization Procedure for Mechanical Component Design Based on Genetic Adaptive Search

Cited by 74 publications

References 4 publications

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Application of Grasshopper Optimization Algorithm for Constrained and Unconstrained Test Functions

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Multiple- and single-objective approaches to laminate optimization with genetic algorithms

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