An Improved Intelligent Ant Colony Algorithm for the Reliability Optimization Problem in Cyber-Physical Systems

Luo, Shiliang; Cheng, Lianglun; Ren, Bin; Zhu, Quanmin

doi:10.4304/jsw.9.1.20-25

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Guo and Diao [27] proposed a kind of improved ant colony algorithm with crossover operator which makes crossover operator among better results at the end of each iteration. Luo et al, [28] proposed an improved intelligent ant colony algorithm to solve the torsion bar optimization problem. Zhang et al, [29] proposed two improvements based on ant colony optimization meta-heuristic constraint solving algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Optimization Algorithm for Solving TSP

Yue¹,

Wang²

2015

IJMUE

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The basic ant colony optimization (ACO) algorithm takes on a longer computing time in the search process and is prone to fall into local optimal solutions, an improved ACO (CEULACO) algorithm is proposed in this paper. In the CEULAC algorithm, the direction guidance and real variable function are used to initialize pheromone concentration according to the path information of undirected graph. The pheromone dynamic evaporation rate strategy is proposed to control the pheromone evaporation in order to increase the global search capability and accelerate the convergence speed. An adaptive dynamic factor is introduced into pheromone updating rule to control the updating proportion of pheromone concentration with optimal solution in single iteration. And the local search strategy is used to improve the quality of the solution and select the current optimal path for global updating the pheromone in order to save some computing time and not reduce the searching efficiency. Some traveling salesman problems are selected to test the performance of the CEULACO algorithm. The simulation experiments show that the improved ACO algorithm can effectively improve the accuracy and the quality of solutions, and distinctly speed up the convergence speed and computing time.

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Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Optimization Algorithm for Solving TSP

Yue¹,

Wang²

2015

IJMUE

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“…In fact, the size of the experimental problems is considered one of the unique features associated with this effort-other efforts work with are unable to provide solutions to problems of this practical size [8] [9]. To this end, larger problems can be pursued in the future.…”

Section: Concluding Commentsmentioning

confidence: 99%

Ant-Colony Optimization for the System Reliability Problem with Quantity Discounts

McMullen¹

2017

AJOR

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This research presents an approach based upon ant-colony optimization to address the system reliability problem. For each component of a system, the number of units in parallel needs to be chosen to maximize the reliability for the entire system. As more parallel units are selected, costs increase in a proportional fashion. For this effort, quantity discounts for additional parallel units are considered, and the budget for purchase of parallel units is limited. Ant colony optimization methodology is employed to find an optimal system reliability that satisfies the budget constraint. The methodology is employed for several test problems, and near-optimal solutions are found.

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