Multiobjective optimization design of hybrid composite laminates for vibration using sequential permutation table method

2021 ◽  
pp. 095440622110504
Author(s):  
Zhao Jing ◽  
Qin Sun ◽  
Yongjie Zhang ◽  
Ke Liang
Keyword(s):  
Multiobjective Optimization ◽  
Fundamental Frequency ◽  
Hybrid Composite ◽  
Composite Laminates ◽  
Optimization Design ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Design Flexibility ◽  
The Cost ◽  
Ply Orientation

Due to the large variable design space in optimization problems of composite laminates, it remains one of the challenging tasks to develop efficient optimization design methods to improve the design flexibility and efficiency. This work presents a sequential permutation table (SPT) method for the multiobjective optimization design of two-material hybrid composite laminates with simply supported boundary conditions, which maximizes the fundamental frequency and minimizes the cost/weight. Based on the vibration analysis of hybrid composite laminates, the approximate linear regularity of the square of fundamental frequency is derived, and two best ply orientations for the two materials are identified, respectively. By assigning one best ply orientation with maximum fundamental frequency at respective stacking positions, and using another best ply orientation to replace plies in the stacking sequence from the mid-plane to the outermost can lead to the optimum. Two multiobjective optimization problems are employed to verify the SPT method, results are compared with those obtained by heuristic algorithms. The obtained better solutions demonstrate the effectiveness and efficiency of the SPT method and its potentials for optimal design of hybrid composite laminates.

An improved proximal method with quasi-distance for nonconvex multiobjective optimization problem

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Fouzia Amir ◽  
Ali Farajzadeh ◽  
Jehad Alzabut
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Convergence Result ◽  
Proximal Method ◽  
Pareto Solution ◽  
Multiobjective Optimization Problems ◽  
Locally Lipschitz ◽  
The Cost ◽  
Constrained Multiobjective Optimization

Abstract Multiobjective optimization is the optimization with several conflicting objective functions. However, it is generally tough to find an optimal solution that satisfies all objectives from a mathematical frame of reference. The main objective of this article is to present an improved proximal method involving quasi-distance for constrained multiobjective optimization problems under the locally Lipschitz condition of the cost function. An instigation to study the proximal method with quasi distances is due to its widespread applications of the quasi distances in computer theory. To study the convergence result, Fritz John’s necessary optimality condition for weak Pareto solution is used. The suitable conditions to guarantee that the cluster points of the generated sequences are Pareto–Clarke critical points are provided.

scholarly journals Application of data mining in multiobjective optimization problems

2014 ◽  
Vol 5 ◽  
pp. A15 ◽  
Author(s):  
Amir Mosavi
Keyword(s):  
Data Mining ◽  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Objective Functions ◽  
Design Variables ◽  
Modeling Techniques ◽  
Practical Engineering ◽  
Meta Modeling ◽  
Classification Tool

In the most engineering optimization design problems, the value of objective functions is not clearly defined in terms of design variables. Instead it is obtained by some numerical analysis such as FE structural analysis, fluid mechanic analysis, and thermodynamic analysis, etc. Usually, these analyses are considerably time consuming to obtain a value of objective functions. In order to make the number of analyses as few as possible a methodology is presented as a supporting tool for the meta-modeling techniques. Researches in meta-modeling for multiobjective optimization are relatively young and there is still much to do. It is shown that visualizing the problem on the basis of the randomly sampled geometrical data of CAD and CAE simulation results, in addition to utilizing classification tool of data mining could be effective as a supporting system to the available meta-modeling techniques. To evaluate the effectiveness of the proposed method a study case in 3D wing design is given. Along with this example, it is discussed how effective the proposed methodology could be in the practical engineering problems.

Evaluation of the Cost Effectiveness of Hybrid Composite Laminates

1977 ◽  
Vol 14 (12) ◽  
pp. 1153-1154 ◽  
Author(s):  
S. V. Kulkarni ◽  
B. W. Rosen
Keyword(s):  
Cost Effectiveness ◽  
Hybrid Composite ◽  
Composite Laminates ◽  
The Cost

scholarly journals Many-Objective Optimization Design of a Public Building for Energy, Daylighting and Cost Performance Improvement

2020 ◽  
Vol 10 (7) ◽  
pp. 2435
Author(s):  
Cheng Sun ◽  
Qianqian Liu ◽  
Yunsong Han
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Public Library ◽  
Building Design ◽  
Energy Performance ◽  
Building Envelope ◽  
Data Conversion ◽  
Cost Performance ◽  
Public Building

The energy performance of buildings especially public buildings needs to be optimized together with environmental, social and cost performance, which can be achieved by the multiobjective optimization method. The traditional building performance simulation (BPS) based multiobjective optimization is time-consuming and inefficient. Practical projects of complex public building design usually involve many-objective optimization problems in which more than three objectives are considered. Using BPS based multiobjective optimization is not sufficient to solve this kind of design problem. This paper aims to propose an artificial neural network (ANN) based many-objective optimization design method, an architect-friendly integrated workflow has been implemented. The proposed method has been applied on a public library building in Changchun city of China to optimize its Energy Use Intensity (EUI), Spatial Daylight Autonomy (sDA), Useful Daylight Illuminance (UDI) and Building Envelope Cost (BEC). The optimization process has obtained 176 non-dominated solutions. By adopting the selected relative optimal solutions, 1.6×105–2.1×105 kWh energy can be saved per year; sDA value and UDI value can be increased by 8.1%–11.0% and 4.3%–4.7% respectively; BEC can be reduced by ¥1.2×105–2.1×105 ($1.7×104–3.0×104). The optimization time has been greatly shortened in this method and the whole process is highly efficient without manual data conversion between different platforms.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Effect of Layering Sequence on Impact Properties of Alkali Treated Phoenix Pusilla Fibers-Glass-Carbon Fabrics Reinforced Hybrid Composite Laminates

2021 ◽  
pp. 1-11
Author(s):  
Madhu Puttegowda ◽  
Sanjay Mavinkere Rangappa ◽  
Anish Khan ◽  
Salma Ahmed Al-Zahrani ◽  
Ahmed Al Otaibi ◽  
...  
Keyword(s):  
Hybrid Composite ◽  
Composite Laminates ◽  
Impact Properties ◽  
Glass Carbon ◽  
Carbon Fabrics

scholarly journals Duality Theorems for (ρ,ψ,d)-Quasiinvex Multiobjective Optimization Problems with Interval-Valued Components

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 894
Author(s):  
Savin Treanţă
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Integral Functional ◽  
Multiple Integral ◽  
Control Problems ◽  
Duality Theorems ◽  
New Class ◽  
Duality Results ◽  
Multiobjective Optimization Problems ◽  
Interval Valued

The present paper deals with a duality study associated with a new class of multiobjective optimization problems that include the interval-valued components of the ratio vector. More precisely, by using the new notion of (ρ,ψ,d)-quasiinvexity associated with an interval-valued multiple-integral functional, we formulate and prove weak, strong, and converse duality results for the considered class of variational control problems.

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