A Hybrid GABC-GA Algorithm for Mechanical Design Optimization Problems

2019 ◽  
pp. -1--1
Author(s):  
Hui Zhi ◽  
Sanyang Liua
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Ga Algorithm

scholarly journals An improved group search optimizer for mechanical design optimization problems

2009 ◽  
Vol 19 (1) ◽  
pp. 91-97 ◽  
Author(s):  
Hai Shen ◽  
Yunlong Zhu ◽  
Ben Niu ◽  
Q.H. Wu
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Group Search

Adaptive firefly algorithm with chaos for mechanical design optimization problems

2015 ◽  
Vol 36 ◽  
pp. 152-164 ◽  
Author(s):  
Adil Baykasoğlu ◽  
Fehmi Burcin Ozsoydan
Keyword(s):  
Design Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Mechanical Design

A Distributed Pool Architecture for Highly Constrained Optimization Problems in Complex Systems Design

2013 ◽  
Vol 13 (3) ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Solution Process ◽  
Systems Design ◽  
Mixed Integer ◽  
Engineering Systems ◽  
Design Problems ◽  
Design Variables ◽  
Complex Engineering

Optimal design of complex engineering systems is challenging because numerous design variables and constraints are present. Dynamic changes in design requirements and lack of complete knowledge of subsystem requirements add to the complexity. We propose an enhanced distributed pool architecture to aid distributed solving of design optimization problems. The approach not only saves solution time but is also resilient against failures of some processors. It is best suited to handle highly constrained design problems, with dynamically changing constraints, where finding even a feasible solution (FS) is challenging. In our work, this task is distributed among many processors. Constraints can be easily added or removed without having to restart the solution process. We demonstrate the efficacy of our method in terms of computational savings and resistance to partial failures of some processors, using two mixed integer nonlinear programming (MINLP)-class mechanical design optimization problems.

Backtracking Search Optimization Algorithm with Low-Discrepancy Sequences for Mechanical Design Optimization Problems

2014 ◽  
Vol 635-637 ◽  
pp. 270-273
Author(s):  
Qiu Yan Xu
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Benchmark Problems ◽  
Initial Population ◽  
Backtracking Search ◽  
Low Discrepancy Sequences ◽  
Search Optimization ◽  
Backtracking Search Optimization Algorithm

This paper presents the backtracking search optimization algorithm with low-discrepancy sequences to solve mechanical design optimization problems involving problem-specific constraints and many different variables. Similar to other evolutionary algorithms, backtracking search optimization algorithm is sensitive to the initial population. Generally speaking, since there is no information about the optimization problem, the initial population should be created uniformly. The low-discrepancy sequences are employed to increase the uniformity of the initial population. The benchmark problems widely used in the literature of mechanical design optimization are used to evaluate the performance of the presented algorithm. Results show that the proposed algorithm is effective and efficient for solving the mechanical design optimization problems considered.

Comparison of Metaheuristic Optimization Algorithms for Solving Constrained Mechanical Design Optimization Problems

2021 ◽  
pp. 115351
Author(s):  
Shubham Gupta ◽  
Hammoudi Abderazek ◽  
Betul Sultan Yıldız ◽  
Ali Riza Yildiz ◽  
Seyedali Mirjalili ◽  
...  
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Algorithms ◽  
Metaheuristic Optimization ◽  
Metaheuristic Optimization Algorithms

An improved particle swarm optimizer for mechanical design optimization problems

2004 ◽  
Vol 36 (5) ◽  
pp. 585-605 ◽  
Author(s):  
S. He ◽  
E. Prempain ◽  
Q. H. Wu
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Particle Swarm ◽  
Particle Swarm Optimizer

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

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