Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is pr...Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.展开更多
The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility ...The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.展开更多
Mixed-integer optimal control problems(MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resu...Mixed-integer optimal control problems(MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resulting in slow convergence or premature convergence by most current heuristic optimization algorithms. Accordingly, this study proposes a new and effective hybrid algorithm based on quantum computing theory to solve the MIOCP. The algorithm consists of two parts:(i) Quantum Annealing(QA) specializes in solving integer optimization with high efficiency owing to the unique annealing process based on quantum tunneling, and(ii) Double-Elite Quantum Ant Colony Algorithm(DEQACA) which adopts double-elite coevolutionary mechanism to enhance global searching is developed for the optimization of continuous decisions. The hybrid QA/DEQACA algorithm integrates the strengths of such algorithms to better balance the exploration and exploitation abilities. The overall evolution performs to seek out the optimal mixed-integer decisions by interactive parallel computing of the QA and the DEQACA. Simulation results on benchmark functions and practical engineering optimization problems verify that the proposed numerical method is more excel at achieving promising results than other two state-of-the-art heuristics.展开更多
针对动态覆盖问题可以转化为多目标优化问题,提出一种解决多目标优化的连续空间蚁群算法(Continuous Space Ant Colony System,CSACS).该算法通过随机划分过程,对连续解空间划分为多个子空间,分别在不同子空间利用蚁群进行区域内以及区...针对动态覆盖问题可以转化为多目标优化问题,提出一种解决多目标优化的连续空间蚁群算法(Continuous Space Ant Colony System,CSACS).该算法通过随机划分过程,对连续解空间划分为多个子空间,分别在不同子空间利用蚁群进行区域内以及区域间搜索Pareto最优解,为了保证最优解的多样性,引入小生境策略进行Pareto最优解适应度更新.实验表明,在不同网络规模和迭代次数下,区域覆盖度和网络寿命相对于传统经典算法有较好改进.字数以250字以上为宜.请不要在摘要中引用参考文献和英文缩略语.展开更多
文摘Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.
基金supported by the National Natural Science Foundation of China (No. 61741102, No. 61471164)China Scholarship Council
文摘The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.
基金supported by the National Natural Science Foundation of China under Grant No.61573378the BUPT Excellent Ph.D.Students Foundation under Grant No.CX2019113。
文摘Mixed-integer optimal control problems(MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resulting in slow convergence or premature convergence by most current heuristic optimization algorithms. Accordingly, this study proposes a new and effective hybrid algorithm based on quantum computing theory to solve the MIOCP. The algorithm consists of two parts:(i) Quantum Annealing(QA) specializes in solving integer optimization with high efficiency owing to the unique annealing process based on quantum tunneling, and(ii) Double-Elite Quantum Ant Colony Algorithm(DEQACA) which adopts double-elite coevolutionary mechanism to enhance global searching is developed for the optimization of continuous decisions. The hybrid QA/DEQACA algorithm integrates the strengths of such algorithms to better balance the exploration and exploitation abilities. The overall evolution performs to seek out the optimal mixed-integer decisions by interactive parallel computing of the QA and the DEQACA. Simulation results on benchmark functions and practical engineering optimization problems verify that the proposed numerical method is more excel at achieving promising results than other two state-of-the-art heuristics.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60674092)江苏省高技术研究项目(the High-Tech Research Program of Jiangsu Province of China under Grant NoBG2006010)
文摘针对动态覆盖问题可以转化为多目标优化问题,提出一种解决多目标优化的连续空间蚁群算法(Continuous Space Ant Colony System,CSACS).该算法通过随机划分过程,对连续解空间划分为多个子空间,分别在不同子空间利用蚁群进行区域内以及区域间搜索Pareto最优解,为了保证最优解的多样性,引入小生境策略进行Pareto最优解适应度更新.实验表明,在不同网络规模和迭代次数下,区域覆盖度和网络寿命相对于传统经典算法有较好改进.字数以250字以上为宜.请不要在摘要中引用参考文献和英文缩略语.
