Genetic simulated annealing algorithm-based assembly sequence planning

2006 ◽  
Author(s):  
Shan Hongbo ◽  
Li Shuxia ◽  
Gong Degang ◽  
Lou Peng
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

An Approach to Assembly Sequence Planning Using Simulated Annealing

2012 ◽  
Vol 457-458 ◽  
pp. 628-634
Author(s):  
Jing Zhang ◽  
Yun Sheng Yang ◽  
Shao Wei Feng
Keyword(s):  
Simulated Annealing ◽  
Energy Function ◽  
Simulated Annealing Algorithm ◽  
Graph Model ◽  
Assembly Sequence Planning ◽  
Assembly Sequence ◽  
Complex Assembly ◽  
Assembly Cost ◽  
Assembly Sequences ◽  
Annealing Algorithm

In order to solve the problem of generating and optimizing the assembly sequences of a complex assembly, the oriented-mating graph model of the assembly and the related mating matrix are created. Then an improved simulated annealing algorithm is used to solve the ASP problem. This algorithm reflects the assembly cost to an energy function associated with the assembly sequence. The energy function is iteratively minimized and occasionally perturbed by a simulated annealing until no further change in the energy occurs. Finally, a living example is given to prove the validity of the method.

scholarly journals Fault Localization Based on Hybrid Genetic Simulated Annealing Algorithm

2021 ◽  
Vol 28 (2) ◽  
pp. 101-109
Keyword(s):  
Simulated Annealing ◽  
Software Testing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Fault Localization ◽  
Good Effect ◽  
Test Case ◽  
Genetic Simulated Annealing Algorithm ◽  
Starting Point ◽  
Annealing Algorithm

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.

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