Searching for the Optimal Arrangement of Mistuned Blades Based on Solving the Traveling Salesman Problem for Simple Mass-Spring Linear System

2021 ◽  
Author(s):  
Nikolay Serebriakov ◽  
Alexander Selivanov

Abstract The article presents an approach to finding the optimal arrangement of blades in a bladed disk based on solving the traveling salesman problem using the ant colony algorithm. The solution to the problem is presented for an equivalent model of the bladed disk. The mistuning parameters are assumed as known — for example, from the data of geometric measurements for each blade from the set used for assembling the bladed disk — considering the influence of the rotation on the investigated resonance mode. Approaches have already been published for the construction of equivalent models and the use of combinatorial optimization methods, including solving the traveling salesman problem, to find the optimal arrangement of the blades. Therefore, this article focuses on demonstrating a simple program implementation of this method. This approach allows a quick assessment of the effect of the blade assembly in the bladed disk on the amplitudes of alternating stresses. It even considers several restarts of the algorithm to determine other possible configurations of the system.

2013 ◽  
Vol 765-767 ◽  
pp. 699-702
Author(s):  
Tian Yuan Zhou

Based on the ant colony algorithm analysis and research, this paper proposed an improved ant colony algorithm. Through updating pheromone and optimal search strategy, then applied to the Traveling Salesman Problem (TSP), effectively improved the searching capability of the algorithm. Finally through the simulation testing and analysis, verified that the improved ant colony algorithm is effective, and has good performance.


2014 ◽  
Vol 4 (4(70)) ◽  
pp. 18
Author(s):  
Ігор Андрійович Могила ◽  
Ірина Іванівна Лобач ◽  
Оксана Андріївна Якимець

2016 ◽  
pp. 1739-1752 ◽  
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. The authors adopt a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.


2015 ◽  
Vol 6 (2) ◽  
pp. 33-44 ◽  
Author(s):  
Hicham El Hassani ◽  
Said Benkachcha ◽  
Jamal Benhra

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. The authors adopt a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.


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