Memetic Computing Applied to the Design of Composite Materials and Structures

Peláez, José Ignacio; Gómez-Ruiz, José Antonio; Veintimilla, Jorge; Vaccaro, Gustavo; Witt, Patricia L.

doi:10.1155/2017/4723863

Cited by 7 publications

(6 citation statements)

References 71 publications

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“…The buckling and failure load factors values for 48 ply laminate and 64 ply laminate are presented in Table 2. The obtained results were compared with the results gained from [6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Bucking Load Optimization Resultsmentioning

confidence: 99%

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Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Majdoubi¹

2020

IJATCSE

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An isotropic mechanical structure cannot withstand shocks, accidents, and mechanical loading. In this perspective, composite structures have been introduced to improve the performance of mechanical structures. We are interested in the study of laminates with the aim to find an optimal structure resistant to vibrations and buckling. To remedy the problem of vibrations and buckling, we use artificial intelligence methods to optimize the design of composite structures. As it is a typical method of artificial intelligence adapted to this studied problem, we use genetic algorithms based on a new genetic operator. In the present article, an optimization procedure based on the new genetic operator called genetic immigration operator is developed to determine the maximum buckling load and fundamental frequency of the laminated plate with plies oriented at -45•/45• ,0•, and 90•. The aim of this paper is the use of two different methods for their effectiveness. These optimization works consist of first maximizing the buckling load factor with UGA (Uniform Genetic Algorithm) and a new evolutionary search strategies called Immigration Genetic: SIG (Standard Immigration Genetic Algorithm) and AIG (Improved Genetic Immigration Algorithm) and second of solving a multi-objective problem with minimizing the cost and weight of the hybrid laminate made of the fibers of two composite materials. The resolution of this problem by the proposed genetic immigration approach is reinforced by the optimization of the CPU computation time which is due to the exploitation of the parallel architecture based on the multi-processor parallel computation.

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Section: Bucking Load Optimization Resultsmentioning

confidence: 99%

“…In addition, TS is a heuristic method that can be applied to solve the laminate optimization problem. It was introduced by [13]. TS is an iterative local search procedure that explores the set of solutions beyond local optimality.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Majdoubi¹

2020

IJATCSE

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“…The memetic operators are then applied to produce offspring. MAs are considered one of the best evolutionary methods that can solve today's increasingly complex and dynamic problems and because of this, they have been extensively applied to solve real-life problems [30,31].…”

Section: Memetic Algorithms (Mas)mentioning

confidence: 99%

Dynamic Street Parking Space Using Memetic Algorithm for Optimization

Akandwanaho¹,

Govender²

2020

Smart Urban Development

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In recent years, there have been an increasing number of automobiles in cities around the world. This is due to more people living and working in cities as a result of urbanization. Street parking remains a common option for motorists, due to it being cheap and convenient. However, this option leads to a high concentration of vehicles causing congestion and obstruction of traffic. This problem is compounded as motorists wait for others to pull out of parking bays or look for empty parking spaces. In order to provide relief to this problem, an intelligent approach is proposed that generates an optimal parking space based on the vehicle location and desired destination. The proposed approach applies its operators adaptively and it derives optimality from the synergy between genetic algorithm and a local search technique in the search optimization process. The proposed method exhibits superior performance when compared with the existing methods over multiple iterations.

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“…The local optimization approaches based on heuristic methods have been one of the first approaches to overcome the limitations of the traditional analytical or gradient-based optimization approaches. 41 The approaches based on hybrid optimization methods and MAs are the ones less explored in the field of computational mechanics, with some relevant references such as GA and SA, 42 GA and gradient-based methods, 43 two local optimization methods (SA and TS), 44 or multiobjective DE and incremental learning, 45,46 which compares multiple combinations of MA, GA, SA, TS, and VNS.…”

Section: Related Workmentioning

confidence: 99%

“…The approaches based on hybrid optimization methods and MAs are the ones less explored in the field of computational mechanics, with some relevant references such as GA and SA, GA and gradient‐based methods, two local optimization methods (SA and TS), or multiobjective DE and incremental learning, which compares multiple combinations of MA, GA, SA, TS, and VNS.…”

Section: Related Workmentioning

confidence: 99%

SoftFEM: The Soft Finite Element Method

Peña

LaTorre

Jérusalem

2019

Numerical Meth Engineering

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Summary While the finite element method (FEM) has now reached full maturity both in academy and industry, its use in optimization pipelines remains either computationally intensive or cumbersome. In particular, currently used optimization schemes leveraging FEM still require the choice of dedicated optimization algorithms for a specific design problem, and a “black box” approach to FEM‐based optimization remains elusive. To this end, we propose here an integrated finite element‐soft computing method, ie, the soft FEM (SoftFEM), which integrates a finite element solver within a metaheuristic search wrapper. To illustrate this general method, we focus here on solid mechanics problems. For these problems, SoftFEM is able to optimize geometry changes and mechanistic measures based on geometry constraints and material properties inputs. From the optimization perspective, the use of a fitness function based on finite element calculation imposes a series of challenges. To bypass the limitations in search capabilities of the usual optimization techniques (local search and gradient‐based methods), we propose, instead a hybrid self adaptive search technique, the multiple offspring sampling (MOS), combining two metaheuristics methods: one population‐based differential evolution method and a local search optimizer. The formulation coupling FEM to the optimization wrapper is presented in detail and its flexibility is illustrated with three representative solid mechanics problems. More particularly, we propose here the MOS as the most versatile search algorithm for SoftFEM. A new method for the identification of nonfully determined parameters is also proposed.

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Memetic Computing Applied to the Design of Composite Materials and Structures

Cited by 7 publications

References 71 publications

Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Artificial Intelligence Approach for Multi-Objective Design Optimization of Composite Structures: Parallel Genetic Immigration

Dynamic Street Parking Space Using Memetic Algorithm for Optimization

SoftFEM: The Soft Finite Element Method

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