scholarly journals Social Network Search for Solving Engineering Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-32
Author(s):  
Hadi Bayzidi ◽  
Siamak Talatahari ◽  
Meysam Saraee ◽  
Charles-Philippe Lamarche
Keyword(s):  
Social Network ◽  
Optimization Problems ◽  
Computational Cost ◽  
Engineering Optimization ◽  
Optimal Solutions ◽  
Network Search ◽  
The Social ◽  
Metaheuristic Optimization Algorithm ◽  
Engineering Optimization Problems ◽  
Better Than

In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

scholarly journals AN INTRODUCTION OF KRILL HERD ALGORITHM FOR ENGINEERING OPTIMIZATION

2015 ◽  
Vol 22 (3) ◽  
pp. 302-310 ◽  
Author(s):  
Amir H. GANDOMI ◽  
Amir H. ALAVI
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
State Of The Art ◽  
Engineering Optimization ◽  
Design Problems ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Metaheuristic Optimization Algorithm ◽  
Engineering Optimization Problems ◽  
Better Than

A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is com­pared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods.

Optimized Radial Basis Function Metamodel for Expensive Engineering Design Optimization

2012 ◽  
Author(s):  
Lv Wang ◽  
Teng Long ◽  
Lei Peng ◽  
Li Liu
Keyword(s):  
Optimization Design ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Method ◽  
Kernel Functions ◽  
Optimization Approach ◽  
Engineering Optimization ◽  
Global Approximation ◽  
Approximation Accuracy ◽  
Engineering Optimization Problems

At the aim of alleviating the computational burden of complicated engineering optimization problems, metamodels have been widely employed to approximate the expensive blackbox functions. Among the popular metamodeling methods RBF metamodel well balances the global approximation accuracy, computational cost and implementation difficulty. However, the approximation accuracy of RBF metamodel is heavily influenced by the width factors of kernel functions, which are hard to determine and actually depend on the numerical behavior of expensive functions and distribution of samples. The main contribution of this paper is to propose an optimized RBF (ORBF) metamodel for the purpose of improving the global approximation capability with an affordable extra computational cost. Several numerical problems are used to compare the global approximation performance of the proposed ORBF metamodeling methods to determine the promising optimization approach. And the proposed ORBF is also adopted in adaptive metamodel-based optimization method. Two numerical benchmark examples and an I-beam optimization design are used to validate the adaptive metamodel-based optimization method using ORBF metamodel. It is demonstrated that ORBF metamodeling is beneficial to improving the optimization efficiency and global convergence capability for expensive 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.

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
Sign in / Sign up

Export Citation Format

Share Document