This paper proposes a Hybridized Ant Colony Optimization (HACO) algorithm. It integrates the advantages of Ant System (AS) and Ant Colony System (ACS) of solving optimization problems. The main focus and core of the H...This paper proposes a Hybridized Ant Colony Optimization (HACO) algorithm. It integrates the advantages of Ant System (AS) and Ant Colony System (ACS) of solving optimization problems. The main focus and core of the HACO algorithm are based on annexing the strengths of the AS, ACO and the Max-Min Ant System (MMAS) previously proposed by various researchers at one time or the order. In this paper, the HACO algorithm for solving optimization problems employs new Transition Probability relations with a Jump transition probability relation which indicates the point or path at which the desired optimum value has been met. Also, it brings to play a new pheromone updating rule and introduces the pheromone evaporation residue that calculates the amount of pheromone left after updating which serves as a guide to the successive ant traversing the path and diverse local search approaches. Regarding the computational efficiency of the HACO algorithm, we observe that the HACO algorithm can find very good solutions in a short time, as the algorithm has been tested on a number of combinatorial optimization problems and results shown to compare favourably with analytical results. This strength can be combined with other metaheuristic approaches in the future work to solve complex combinatorial optimization problems.展开更多
When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristi...When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.展开更多
The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was ...The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.展开更多
The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blo...The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.展开更多
核事故的发生具有不可预测性和破坏性,为应急车辆制定合理的疏散计划将危险区域的人员撤离至安置点,可以有效减少人员所受到的伤害。针对核事故下应急车辆路径规划问题,以累积辐射剂量为评价指标,提出了一种基于混合蚁群算法(Hybrid ant...核事故的发生具有不可预测性和破坏性,为应急车辆制定合理的疏散计划将危险区域的人员撤离至安置点,可以有效减少人员所受到的伤害。针对核事故下应急车辆路径规划问题,以累积辐射剂量为评价指标,提出了一种基于混合蚁群算法(Hybrid ant colony algorithm,HACO)的车辆路径规划方法。首先,利用模糊网络建立了时间窗内疏散路径平均通行时间期望模型,同时结合累积辐射剂量计算模型,建立了能够随时间变化的动态累积辐射剂量计算模型。然后在蚁群算法迭代过程中引入模拟退火算法,并且在邻域搜索中引入A*算法启发式思想,提高了算法全局寻优能力。为进一步提高算法的局部搜索能力,引入帕累托排序方式,在蚁群算法信息素更新方式中加入距离对信息素增量的影响。仿真结果表明:HACO算法相较于蚁群算法平均收敛值提高了31%,稳定性提高了30%,能够为核事故下疏散路径规划预案的制定提供技术支持。展开更多
文摘This paper proposes a Hybridized Ant Colony Optimization (HACO) algorithm. It integrates the advantages of Ant System (AS) and Ant Colony System (ACS) of solving optimization problems. The main focus and core of the HACO algorithm are based on annexing the strengths of the AS, ACO and the Max-Min Ant System (MMAS) previously proposed by various researchers at one time or the order. In this paper, the HACO algorithm for solving optimization problems employs new Transition Probability relations with a Jump transition probability relation which indicates the point or path at which the desired optimum value has been met. Also, it brings to play a new pheromone updating rule and introduces the pheromone evaporation residue that calculates the amount of pheromone left after updating which serves as a guide to the successive ant traversing the path and diverse local search approaches. Regarding the computational efficiency of the HACO algorithm, we observe that the HACO algorithm can find very good solutions in a short time, as the algorithm has been tested on a number of combinatorial optimization problems and results shown to compare favourably with analytical results. This strength can be combined with other metaheuristic approaches in the future work to solve complex combinatorial optimization problems.
文摘When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.
基金National Natural Science Foundation of China(No.70971020)the Subject of Ministry of Education of Hunan Province,China(No.13C818)+3 种基金the Project of Industrial Science and Technology Support of Hengyang City,Hunan Province,China(No.2013KG63)the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science and Engineering,China(No.2012RYJ03)the Fund Project of Humanities and Social Sciences,Ministry of Education of China(No.13YJCZH147)the Special Fund for Shanghai Colleges' Outstanding Young Teachers' Scientific Research Projects,China(No.ZZGJD12033)
文摘The traveling salesman problem( TSP) is a well-known combinatorial optimization problem as well as an NP-complete problem. A dynamic multi-swarm particle swarm optimization and ant colony optimization( DMPSO-ACO) was presented for TSP.The DMPSO-ACO combined the exploration capabilities of the dynamic multi-swarm particle swarm optimizer( DMPSO) and the stochastic exploitation of the ant colony optimization( ACO) for solving the traveling salesman problem. In the proposed hybrid algorithm,firstly,the dynamic swarms,rapidity of the PSO was used to obtain a series of sub-optimal solutions through certain iterative times for adjusting the initial allocation of pheromone in ACO. Secondly,the positive feedback and high accuracy of the ACO were employed to solving whole problem. Finally,to verify the effectiveness and efficiency of the proposed hybrid algorithm,various scale benchmark problems were tested to demonstrate the potential of the proposed DMPSO-ACO algorithm. The results show that DMPSO-ACO is better in the search precision,convergence property and has strong ability to escape from the local sub-optima when compared with several other peer algorithms.
文摘The recent aggrandizement of radio frequency(RF)signals in wireless power transmission combined with energy harvesting methods have led to the replacement of traditional battery-powered wireless networks since the blooming RF technology provides energy renewal of wireless devices with the quality of service(QoS).In addition,it does not require any unnecessary alterations on the transmission hardware side.A hybridized global optimization technique uniting Global best and Local best(GL)based particle swarm optimization(PSO)and ant colony optimization(ACO)is proposed in this paper to optimally allocate resources in wireless powered communication networks(WPCN)through coordinated operation of communication groups,in which the wireless energy transfer and information sharing take place concomitantly by the aid of a cooperative relay positioned in between the communicating groups.The designed algorithm assists in minimizing power consumption and maximizes the weighted sum rate at the end-user side.Thus the principal target of the system is coordinated optimization of energy beamforming along with time and energy allocation to reduce the total energy consumed combined with assured information rates of the communication groups.Numerical outputs are presented to manifest the proposed system’s performance to verify the analytical results via simulations.
文摘核事故的发生具有不可预测性和破坏性,为应急车辆制定合理的疏散计划将危险区域的人员撤离至安置点,可以有效减少人员所受到的伤害。针对核事故下应急车辆路径规划问题,以累积辐射剂量为评价指标,提出了一种基于混合蚁群算法(Hybrid ant colony algorithm,HACO)的车辆路径规划方法。首先,利用模糊网络建立了时间窗内疏散路径平均通行时间期望模型,同时结合累积辐射剂量计算模型,建立了能够随时间变化的动态累积辐射剂量计算模型。然后在蚁群算法迭代过程中引入模拟退火算法,并且在邻域搜索中引入A*算法启发式思想,提高了算法全局寻优能力。为进一步提高算法的局部搜索能力,引入帕累托排序方式,在蚁群算法信息素更新方式中加入距离对信息素增量的影响。仿真结果表明:HACO算法相较于蚁群算法平均收敛值提高了31%,稳定性提高了30%,能够为核事故下疏散路径规划预案的制定提供技术支持。
