Weight Minimization Problem of 2-Dimensional Trusses under Constraints of Stresses and Frequencies : Discussion on Solving Method by Using Genetic Algorithm

2000 ◽  
Vol 2000.2 (0) ◽  
pp. 505-506
Author(s):  
Tkaaki MIYAMOTO ◽  
Hiroshi HASEGAWA ◽  
Tatsushi Sato ◽  
Keishi KAWAMO
Keyword(s):  
Genetic Algorithm ◽  
Minimization Problem ◽  
Weight Minimization

scholarly journals Weight Minimization of Spatial Trusses with Genetic Algorithm

2019 ◽  
Vol 1 (1) ◽  
pp. 238-243
Author(s):  
Maksym Grzywiński ◽  
Jacek Selejdak
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Minimization Problem ◽  
Truss Structures ◽  
Shape Design ◽  
Jaya Algorithm ◽  
Finite Element Code ◽  
Simple Formulation ◽  
Weight Minimization ◽  
Design Variables

Abstract A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.

scholarly journals Quantum­Inspired Genetic Algorithm for Solving the Test Suite Minimization Problem

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Minimization Problem ◽  
Test Suite ◽  
Test Cases ◽  
Special Importance ◽  
Quantum Superposition ◽  
Complete Problem ◽  
Minimal Subset ◽  
Np Complete ◽  
Test Requirements

Test Suite Minimization problem is a nondeterministic polynomial time (NP) complete problem insoftware engineering that has a special importance in software testing. In this problem, a subset with a minimalsize that contains a number of test cases that cover all the test requirements should be found. A brute­forceapproach to solving this problem is to assume a size for the minimal subset and then search to find if there is asubset of test cases with the assumed size that solves the problem. If not, the assumed minimal size is graduallyincremented, and the search is repeated. In this paper, a quantum­inspired genetic algorithm (QIGA) will beproposed to solve this problem. In it, quantum superposition, quantum rotation and quantum measurement willbe used in an evolutionary algorithm. The paper will show that the adopted quantum techniques can speed upthe convergence of the classical genetic algorithm. The proposed method has an advantage in that it reduces theassumed minimal number of test cases using quantum measurements, which makes it able to discover the minimalnumber of test cases without any prior assumptions.

scholarly journals Genetic Algorithm Based Resource Minimization in Network Code Based Peer-to-Peer Network

2020 ◽  
pp. 2150092
Author(s):  
M. Anandaraj ◽  
K. Selvaraj ◽  
P. Ganeshkumar ◽  
K. Rajkumar ◽  
K. Sriram
Keyword(s):  
Genetic Algorithm ◽  
Minimization Problem ◽  
Dynamic Change ◽  
Network Code ◽  
System Throughput ◽  
Hard Problem ◽  
Np Hard ◽  
Interesting Study ◽  
Np Hard Problem ◽  
Resource Minimization

Block scheduling is difficult to implement in P2P network since there is no central coordinator. This problem can be solved by employing network coding technique which allows intermediate nodes to perform the coding operation instead of store and forward the received data. There is a general assumption in this area of research so far that a target download rate is always attainable at every peer as long as coding operation is performed at all the nodes in the network. An interesting study is made that a maximum download rate can be attained by performing the coding operation at relatively small portion of the network. The problem of finding the minimal set of node to perform the coding operation and links to carry the coded data is called as a network code minimization problem (NCMP). It is proved to be an NP hard problem. It can be solved using genetic algorithm (GA) because GA can be used to solve the diverse NP hard problem. A new NCMP model which considers both minimize the resources needed to perform coding operation and dynamic change in network topology due to disconnection is proposed. Based on this new NCMP model, an effective and novel GA is proposed by implementing problem specific GA operators into the evolutionary process. There is an attempt to implement the different compositions and several options of GA elements which worked well in many other problems and pick the one that works best for this resource minimization problem. Our simulation results prove that the proposed system outperforms the random selection and coding at all possible node mechanisms in terms of both download time and system throughput.

Weight minimization of trusses with genetic algorithm

2011 ◽  
Vol 11 (2) ◽  
pp. 2565-2575 ◽  
Author(s):  
Tayfun Dede ◽  
Serkan Bekiroğlu ◽  
Yusuf Ayvaz
Keyword(s):  
Genetic Algorithm ◽  
Weight Minimization
Sign in / Sign up

Export Citation Format

Share Document