Reliability Optimization for Multi-State Series-Parallel System Design Using Ant Colony Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 133-137 ◽  
Author(s):  
Peng Di ◽  
Yi Fan Xu ◽  
Fang Li ◽  
Tong Chen
Keyword(s):  
System Reliability ◽  
Ant Colony Algorithm ◽  
State System ◽  
Ant Colony ◽  
Reliability Optimization ◽  
Optimal Solutions ◽  
Performance Constraints ◽  
And Performance ◽  
Nominal Performance ◽  
Apportionment Problem

In this paper, an algorithm based ant colony approach (ACA) is designed to find the optimal configuration for redundancy apportionment problem (RAP) of series-parallel multi-state system (MSS). In order to achieve maximum system reliability under cost and performance constraints, the algorithm takes advantage of ACA’s combinatorial optimization, uses heuristic information about reliability to arrange components from available choice. Different from traditional nominal performance and reliability system, universal generating function (UGF) is introduced and used to estimate the multi-state system reliability. Finally the experiment has shown that the presented algorithm can provide optimal and near optimal solutions, and have efficient and convenient calculation performance.

Optimization on NoC Mapping Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 539 ◽  
pp. 280-285 ◽  
Author(s):  
Dong Li
Keyword(s):  
Power Consumption ◽  
Ant Colony Algorithm ◽  
Simulation Experiment ◽  
Local Optimization ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Optimization Scheme ◽  
Tabu List ◽  
Mapping Algorithm ◽  
Improved Ant Colony Algorithm

Traditional ant colony mapping algorithm not only has big power consumption, but also is easy to be trapped into local optimization on NoC mapping, for which the paper proposes an optimization scheme based on improved ant colony algorithm. Firstly, the parameters are for initialization operation. Secondly, tabu list is used to solve them, and the solutions are for local optimization of optimal solutions by using 2-opt algorithm. Lastly, pheromone rules are updated. Simulation experiment indicates that compared with traditional ant colony mapping algorithm, NoC mapping optimization scheme based on improved ant colony algorithm not only has better performance on mapping power consumption, but also is not easy to be trapped into local optimization.

scholarly journals Modular Robotic Design and Reconfiguring Path Planning

2022 ◽  
Vol 12 (2) ◽  
pp. 723
Author(s):  
Ye Dai ◽  
Chao-Fang Xiang ◽  
Zhao-Xu Liu ◽  
Zhao-Long Li ◽  
Wen-Yin Qu ◽  
...  
Keyword(s):  
Path Planning ◽  
Industrial Application ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Configuration Design ◽  
Functional Modules ◽  
Simulation Experiments ◽  
Innovative Design ◽  
And Performance ◽  
Robot Configurations

The modular robot is becoming a prevalent research object in robots because of its unique configuration advantages and performance characteristics. It is possible to form robot configurations with different functions by reconfiguring functional modules. This paper focuses on studying the modular robot’s configuration design and self-reconfiguration process and hopes to realize the industrial application of the modular self-reconfiguration robot to a certain extent. We design robotic configurations with different DOF based on the cellular module of the hexahedron and perform the kinematic analysis of the structure. An innovative design of a modular reconfiguration platform for conformational reorganization is presented, and the collaborative path planning between different modules in the reconfiguration platform is investigated. We propose an optimized ant colony algorithm for reconfiguration path planning and verify the superiority and rationality of this algorithm compared with the traditional ant colony algorithm for platform path planning through simulation experiments.

scholarly journals An ant colony algorithm for universiti sultan zainal abidin examination timetabling problem

2019 ◽  
Vol 13 (1) ◽  
pp. 191
Author(s):  
Ahmad Firdaus Khair ◽  
Mokhairi Makhtar ◽  
Munirah Mazlan ◽  
Mohamad Afendee Mohamed ◽  
Mohd Nordin Abdul Rahman
Keyword(s):  
Ant Colony Algorithm ◽  
Educational Institution ◽  
Real Life ◽  
Ant Colony ◽  
Ant Colony Optimisation ◽  
Examination Timetabling ◽  
Timetabling Problem ◽  
College And University ◽  
Academic Management ◽  
And Performance

The real-life construction of examination timetabling problem is considered as a common problem that always encountered and experienced in educational institution whether in school, college, and university. This problem is usually experienced by the academic management department where they have trouble to handle complexity for assign examination into a suitable timeslot manually. In this paper, an algorithm approach of ant colony optimisation (ACO) is presented to find an effective solution for dealing with Universiti Sultan Zainal Abidin (UniSZA) examination timetabling problems. A combination of heuristic with ACO algorithm contributes the development solution in order to simplify and optimize the pheromone occurrence of matrix updates which include the constraints problem. The implementation of real dataset instances from academic management is applied to the approach for generating the result of examination timetable. The result and performance that obtained will be used for further use to evaluate the quality and observe the solution whether our examination timetabling system is reliable and efficient than the manual management that can deal the constraints problem.

scholarly journals A Hybrid Ant Colony Algorithm for Quadratic Assignment Problem

2013 ◽  
Vol 7 (1) ◽  
pp. 51-54 ◽  
Author(s):  
Guo Hong
Keyword(s):  
Ant Colony Optimization ◽  
Assignment Problem ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Quadratic Assignment Problem ◽  
Ant Colony ◽  
Quadratic Assignment ◽  
Optimal Solutions ◽  
Combinatorial Optimization Problems

Quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in many fields. Many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Ant colony algorithm is a multi-agent system inspired by behaviors of real ant colonies to solve optimization problems. Ant colony optimization (ACO) is one of new bionic optimization algorithms and it has some characteristics such as parallel, positive feedback and better performances. ACO has achieved in solving quadratic assignment problems. However, its solution quality and its computation performance need be improved for a large scale QAP. In this paper, a hybrid ant colony optimization (HACO) has been proposed based on ACO and particle swarm optimization (PSO) for a large scale QAP. PSO algorithm is combined with ACO algorithm to improve the quality of optimal solutions. Simulation experiments on QAP standard test data show that optimal solutions of HACO are better than those of ACO for QAP.

Improved Ant Colony Algorithm and Application in Sequence Images of Prostate DWI Registration

2014 ◽  
Vol 1049-1050 ◽  
pp. 530-534
Author(s):  
Xiao Ping Zong ◽  
Hai Bin Zhang ◽  
Lei Hao ◽  
Pei Guang Wang
Keyword(s):  
Continuous Function ◽  
Diffusion Weighted Imaging ◽  
Ant Colony Algorithm ◽  
Transition Probabilities ◽  
Function Optimization ◽  
Experimental Results ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Ant System ◽  
Improved Ant Colony Algorithm

Because of the drift which exists in sequence image of prostate DWI (Diffusion Weighted Imaging), the global ant colony algorithm is introduced into the paper for registration optimization. The paper introduces an ant colony algorithm for continuous function optimization, based on max-min ant system (MMAS). This paper controls the transition probabilities and enhances the abilities of ants seeking globally optimal solutions by adding an adjustable factor in the basic ant colony algorithm and updating the local pheromone and global pheromone. Experimental results verify the effectiveness of the algorithm.

Reliability optimization using ant colony algorithm under performance and cost constraints

2005 ◽  
Vol 76 (1-3) ◽  
pp. 1-8 ◽  
Author(s):  
R. Meziane ◽  
Y. Massim ◽  
A. Zeblah ◽  
A. Ghoraf ◽  
R. Rahli
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Reliability Optimization ◽  
Cost Constraints
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