A New Pipe Routing Approach for Aero-Engines by Octree Modeling and Modified Max-Min Ant System Optimization Algorithm

2016 ◽  
Vol 34 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Y.-F. Qu ◽  
D. Jiang ◽  
X.-L. Zhang
Keyword(s):  
Optimization Algorithm ◽  
System Optimization ◽  
Ant System ◽  
Solution Quality ◽  
Dynamic Update ◽  
Pipe Routing ◽  
Efficiency And Effectiveness ◽  
Speed Up ◽  
Aero Engines ◽  
Number Of Bends

AbstractAero-engines usually contain a lot of pipes and cables which have an important influence on product performance and reliability. In this paper, a new pipe routing approach for aero-engines is proposed. First, an adaptive octree modeling method is presented according to the characteristics of the layout space. After considering three types of engineering constraints, the total length of pipelines, the total number of bends and the natural frequency of pipelines are modeled as the optimal objective. Then, a Modified Max-Min Ant System optimization algorithm (MMMAS), which uses layered node selection and dynamic update mechanism, is proposed for pipe routing. For branch pipelines, ant colony searches in groups and parallel to improve the solution quality and speed up the convergence greatly. Finally, numerical comparisons with other current approaches in literatures demonstrate the efficiency and effectiveness of the proposed approach. And a case study of pipe routing for aero-engines is conducted to validate this approach.

Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design

2021 ◽  
Vol 63 (3) ◽  
pp. 266-271
Author(s):  
Hammoudi Abderazek ◽  
Ferhat Hamza ◽  
Ali Riza Yildiz ◽  
Sadiq M. Sait
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spur Gear ◽  
Comparative Investigation ◽  
Whale Optimization Algorithm ◽  
Solution Quality ◽  
Gear Design ◽  
Geometric Conditions ◽  
Swarm Algorithms ◽  
Whale Optimization

Abstract In this study, two recent algorithms, the whale optimization algorithm and moth-flame optimization, are used to optimize spur gear design. The objective function is the minimization of the total weight of the spur gear pair. Moreover, the optimization problem is subjected to constraints on the main kinematic and geometric conditions as well as to the resistance of the material of the gear system. The comparison between moth-flame optimization (MFO), the whale optimization algorithm (WOA), and previous studies indicate that the final results obtained from both algorithms lead to a reduction in gear weight by 1.05 %. MFO and the WOA are compared with four additional swarm algorithms. The experimental results indicate that the algorithms introduced here, in particular MFO, outperform the four other methods when compared in terms of solution quality, robustness, and high success rate.

An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

2013 ◽  
Vol 389 ◽  
pp. 849-853
Author(s):  
Fang Song Cui ◽  
Wei Feng ◽  
Da Zhi Pan ◽  
Guo Zhong Cheng ◽  
Shuang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Volatility Model ◽  
Local Optimal Solution ◽  
The Impact ◽  
Improved Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

Research in Multidisciplinary Collaborative Optimization Base on Symmetric Fuzzy Satisfaction

2012 ◽  
Vol 616-618 ◽  
pp. 2175-2181
Author(s):  
Nan Li ◽  
Bao Wei Song
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
System Optimization ◽  
Collaborative Optimization ◽  
System Level ◽  
Academic Level ◽  
Multi Level ◽  
The Impact

Multidisciplinary collaborative optimization (CO) is a multi-level optimization algorithm, which include system-level and academic level. Because these are many complications in Engineering, it will exist the fuzzy in Discipline-level optimization devices. If the impact of discipline-level ambiguity was ignored, it will lead to some big errors in the system optimization. In order to solve this problem, this paper introduced the optimal fuzzy satisfaction into multidisciplinary collaborative optimization design, and made it as optimal targets of sub-discipline optimization devices. Then a method of multidisciplinary collaborative optimization base on Fuzzy Satisfaction was get., which can solve the fuzzy problem in multidisciplinary collaborative optimization. Meanwhile this paper used this method in multidisciplinary collaborative optimization of torpedo, and compared the results that was get by this method with the results which was get by the method that did not base on the fuzzy satisfaction, finally get the conclusion that the method is fit to Multidisciplinary collaborative optimization of torpedo.

A Kind of Evolutionary Multi-Objective Optimization Algorithm Based on AIS

2013 ◽  
Vol 442 ◽  
pp. 419-423
Author(s):  
Ming Song Li
Keyword(s):  
Immune System ◽  
Optimization Algorithm ◽  
Clonal Selection ◽  
Research Area ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Objective Space ◽  
Speed Up ◽  
Important Research Area ◽  
Intelligent Information

Problem of multi-objective optimization based on Artificial Immune System (AIS) is an important research area of current evolutionary computing. Starting from the intelligent information processing mechanism of immune theory and the immune system itself, a kind of evolutionary multi-objective optimization algorithm based on AIS is proposed. Clonal selection, scattered crossover and hypermutation based on the learning mechanism are characteristics of the algorithm. Algorithm implements clonal selection according to the distribution of individuals in the objective space, which benefit obtaining Pareto optimal boundary distributed more widely and speed up the convergence. Compared with the existing algorithms, the algorithm has been greatly improved in convergence, diversity, and distribution of solutions.

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