Study on Mix-Variable Collaborative Design Optimization

In order to deal with complex product design optimization problems with both discrete and continuous variables, mix-variable collaborative design optimization algorithm is put forward based on collaborative optimization, which is an efficient way to solve mix-variable design optimization problems. On the rule of “divide and rule”, the algorithm decouples the problem into some relatively simple subsystems. Then by using collaborative mechanism, the optimal solution is obtained. Finally, the result of a case shows the feasibility and effectiveness of the new algorithm.

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Process Control Method of Complex Product Multidisciplinary Collaborative Design System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.2888 ◽

2013 ◽

Vol 712-715 ◽

pp. 2888-2893

Author(s):

Hai Qiang Liu ◽

Ming Lv

Keyword(s):

Information Sharing ◽

Product Design ◽

Design Process ◽

Collaborative Design ◽

Control Method ◽

Integrated System ◽

Design System ◽

System Control ◽

Complex Product ◽

Product Design Process

In order to realize information sharing and interchange of complex product multidisciplinary collaborative design (MCD) design process and resources. The Process integrated system control of product multidisciplinary collaborative design was analyzed firstly in this paper, then design process of complex product for supporting multidisciplinary collaborative was introduced, a detailed description is given of the organization structure and modeling process of MCD-oriented Integration of Product Design Meta-model ; and concrete implement process of process integrated system control method was introduced to effectively realize information sharing and interchange between product design process and resources.

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An evolutionary-based optimization algorithm for truss sizing design

Vietnam Journal of Mechanics ◽

10.15625/0866-7136/7476 ◽

2016 ◽

Vol 38 (4) ◽

pp. 307-317

Author(s):

Pham Hoang Anh

Keyword(s):

Local Search ◽

Objective Function ◽

Optimization Algorithm ◽

Optimization Problems ◽

Optimal Solution ◽

The Other ◽

Truss Structures ◽

Benchmark Problems ◽

Discrete Variables ◽

Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

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Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406211432981 ◽

2012 ◽

Vol 226 (9) ◽

pp. 2281-2295 ◽

Cited By ~ 5

Author(s):

Mohammad Reza Farmani ◽

Jafar Roshanian ◽

Meisam Babaie ◽

Parviz M Zadeh

Keyword(s):

Particle Swarm Optimization ◽

Design Optimization ◽

Multidisciplinary Design Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Collaborative Optimization ◽

Multidisciplinary Design ◽

Fuzzy Decision ◽

Swarm Optimization ◽

Multi Objective

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

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A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch021 ◽

2017 ◽

pp. 491-521 ◽

Cited By ~ 2

Author(s):

Rizk M. Rizk-Allah ◽

Aboul Ella Hassanien

Keyword(s):

Optimization Algorithm ◽

Firefly Algorithm ◽

Optimization Problems ◽

Optimal Solution ◽

Hybrid Optimization ◽

Multi Objective Optimization ◽

Local Optima ◽

Fruit Fly Optimization ◽

Multi Objective ◽

Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

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Black-box combinatorial optimization using models with integer-valued minima

Annals of Mathematics and Artificial Intelligence ◽

10.1007/s10472-020-09712-4 ◽

2020 ◽

Author(s):

Laurens Bliek ◽

Sicco Verwer ◽

Mathijs de Weerdt

Keyword(s):

Optimization Algorithm ◽

Surrogate Model ◽

Optimization Problems ◽

Random Search ◽

Continuous Optimization ◽

Optimal Solution ◽

Black Box ◽

Combinatorial Problems ◽

Bayesian Optimization ◽

Bayesian Optimization Algorithm

Abstract When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables.

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Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

Journal of Algorithms & Computational Technology ◽

10.1177/1748301816665521 ◽

2016 ◽

Vol 11 (1) ◽

pp. 58-67 ◽

Cited By ~ 5

Author(s):

S Sarathambekai ◽

K Umamaheswari

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Optimization Problems ◽

Particle Swarm ◽

Optimal Solution ◽

Initial Population ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

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Hierarchical Arrangement of Characteristics in Product Design Optimization Problems for Deeper Insight Into Optimized Results

Volume 1: 30th Design Automation Conference ◽

10.1115/detc2004-57366 ◽

2004 ◽

Author(s):

Masataka Yoshimura ◽

Masahiko Taniguchi ◽

Kazuhiro Izui ◽

Shinji Nishiwaki

Keyword(s):

Design Optimization ◽

Product Design ◽

Optimum Design ◽

Design Problem ◽

Optimization Problem ◽

Optimization Problems ◽

Optimization Method ◽

Hierarchical Level ◽

Hierarchical Arrangement ◽

Insight Into

This paper proposes a design optimization method for machine products that is based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, to accommodate the specific features or difficulties of a particular design problem. The optimization problem is expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively enables achieving optimum design solutions, but also facilitates deeper insight into the design optimization results, and aids obtaining ideas for breakthroughs in the optimum solutions. An applied example is given to demonstrate the effectiveness of the proposed method.

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Optimization Method Based on a Mix Strategy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.816-817.1154 ◽

2013 ◽

Vol 816-817 ◽

pp. 1154-1157

Author(s):

Xu Yin ◽

Ai Min Ji

Keyword(s):

Optimization Problems ◽

Optimal Solution ◽

Optimization Method ◽

Local Solution ◽

Collaborative Optimization ◽

Design Variables ◽

Gradient Optimization ◽

Local Optimal Solution ◽

Optimal Values ◽

Low Efficiency

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.

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