A novel ameliorated Harris hawk optimizer for solving complex engineering optimization problems

2021 ◽  
Author(s):  
Sheila Mahapatra ◽  
Bishwajit Dey ◽  
Saurav Raj
Keyword(s):  
Optimization Problems ◽  
Engineering Optimization ◽  
Complex Engineering ◽  
Engineering Optimization Problems

Multi-Point Sequential Sampling Method for Complex Engineering Optimization Problems

2012 ◽  
Vol 201-202 ◽  
pp. 78-82
Author(s):  
Si Liang Zhang ◽  
Ping Zhu ◽  
Wei Chen
Keyword(s):  
Prediction Error ◽  
Sampling Method ◽  
Optimization Problems ◽  
Sequential Sampling ◽  
Efficient Global Optimization ◽  
Engineering Optimization ◽  
Highly Nonlinear ◽  
Complex Engineering ◽  
Kriging Predictor ◽  
Engineering Optimization Problems

Metamodeling techniques are commonly used to replace expensive computer simulations in complex engineering optimization problems. Due to the discrepancy between the simulation model and metamodel, the prediction error in predicted responses may lead to a wrong solution. To balance the predicted mean and prediction error, the efficient global optimization (EGO) algorithm using Kriging predictor can be used to explore the design space and find next sample to adaptively improve the fitting accuracy of the predicted responses. However in conventional EGO algorithm, adding one point per iteration may be not efficient for the complex engineering problems. In this paper, a new multi-point sequential sampling method is proposed to include multiple points per iteration. To validate the benefits of the proposed multi-point sequential sampling method, a mathematical example and a highly-nonlinear automotive crashworthiness design example are illustrated. Results show that the proposed method can efficiently mitigate the prediction error and find the global optimum using fewer iterations.

scholarly journals High-Performance Computing Strategies for Complex Engineering Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-1
Author(s):  
Gongnan Xie ◽  
Massimo Scalia ◽  
Masoud Rokni ◽  
Balaji Raghavan ◽  
Manyu Xiao
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Complex Engineering ◽  
Performance Computing ◽  
Engineering Optimization Problems

scholarly journals Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1092
Author(s):  
Qing Duan ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Xingping Sun ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Chaotic Sequence ◽  
Engineering Optimization ◽  
Exploration And Exploitation ◽  
Salp Swarm Algorithm ◽  
Symmetric Perturbation ◽  
Swarm Algorithm ◽  
Search Capability ◽  
Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm(SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

An enhanced hybrid and adaptive meta-model based global optimization algorithm for engineering optimization problems

2016 ◽  
Vol 59 (8) ◽  
pp. 1147-1155 ◽  
Author(s):  
Guan Zhou ◽  
LiBin Duan ◽  
WanZhong Zhao ◽  
ChunYan Wang ◽  
ZhengDong Ma ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Global Optimization Algorithm ◽  
Meta Model ◽  
Model Based ◽  
Engineering Optimization Problems

Parameter Sensitivity Analysis of a Large-Scale System With Several Components Interacting in Series

1989 ◽  
Author(s):  
H. Torab
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Optimization Problems ◽  
Parameter Sensitivity ◽  
Engineering Optimization ◽  
Parameter Sensitivity Analysis ◽  
Large Scale Systems ◽  
In Series ◽  
Special Case ◽  
Engineering Optimization Problems

Abstract Parameter sensitivity for large-scale systems that include several components which interface in series is presented. Large-scale systems can be divided into components or sub-systems to avoid excessive calculations in determining their optimum design. Model Coordination Method of Decomposition (MCMD) is one of the most commonly used methods to solve large-scale engineering optimization problems. In the Model Coordination Method of Decomposition, the vector of coordinating variables can be partitioned into two sub-vectors for systems with several components interacting in series. The first sub-vector consists of those variables that are common among all or most of the elements. The other sub-vector consists of those variables that are common between only two components that are in series. This study focuses on a parameter sensitivity analysis for this special case using MCMD.

scholarly journals Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

2021 ◽  
Vol 67 (3) ◽  
pp. 2845-2862
Author(s):  
Muhammad Asif Jan ◽  
Yasir Mahmood ◽  
Hidayat Ullah Khan ◽  
Wali Khan Mashwani ◽  
Muhammad Irfan Uddin ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Constraint Handling ◽  
Engineering Optimization ◽  
Engineering Optimization Problems
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