针对灰狼优化(Grey wolf optimization,GWO)算法在处理复杂优化问题时优化精度不高,易陷于局部最优等问题,提出了一种强化狼群等级制度的灰狼优化(GWO based on strengthening the hierarchy of wolves,GWOSH)算法。该算法为灰狼个体设...针对灰狼优化(Grey wolf optimization,GWO)算法在处理复杂优化问题时优化精度不高,易陷于局部最优等问题,提出了一种强化狼群等级制度的灰狼优化(GWO based on strengthening the hierarchy of wolves,GWOSH)算法。该算法为灰狼个体设置了跟随狩猎和自主探索两种狩猎模式,并根据自身等级情况来控制选择狼群的狩猎模式。在跟随狩猎模式中,灰狼个体以等级高于自身的灰狼的位置信息来指引自己到达最优解区域;而在自主探索模式中,灰狼个体会同时审视等级高于自身的灰狼的位置信息和自身位置信息,并基于这些信息自主判断猎物的位置,同时两种更新模式都将引入优胜劣汰选择规则来确保种群的狩猎方向。对12个基准测试函数进行优化的结果表明:与已有的算法相比,GWOSH算法的全局搜索能力更强,更能有效避免易早熟收敛的问题,更适用于求解高维的复杂优化问题。展开更多
In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using...In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.展开更多
In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple ...In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.展开更多
This paper explores the application of noncooperative game theory together with the concept of Nash equilibrium to the investigation of some basic problems on multi-scale structure, especially the meso-scale structure...This paper explores the application of noncooperative game theory together with the concept of Nash equilibrium to the investigation of some basic problems on multi-scale structure, especially the meso-scale structure in the multi-phase complex systems in chemical engineering. The basis of this work is the energy-minimization-multi-scale （EMMS） model proposed by Li and Kwauk （1994） and Li, et al. （2013） which identifies the multi-scale structure as a result of ＇compromise-in-competition between dominant mechanisms＇ and tries to solve a multi-objective optimization problem. However, the existing methods often integrate it into a problem of single objective optimization, which does not clearly reflect the ＇compromise-in-competition＇ mechanism and causes heavy computation burden as well as uncertainty in choosing suitable weighting factors. This paper will formulate the compromise in competition mechanism in EMMS model as a noncooperative game with constraints, and will describe the desired stable system state as a generalized Nash equilibrium. Then the authors will investigate the game theoretical approach for two typical systems in chemical engineering, the gas-solid fluidiza- tion （GSF） system and turbulent flow in pipe. Two different cases for generalized Nash equilibrinm in such systems will be well defined and distinguished. The generalize Nash equilibrium will be solved accurately for the GSF system and a feasible method will be given for turbulent flow in pipe. These results coincide with the existing computational results and show the feasibility of this approach, which overcomes the disadvantages of the existing methods and provides deep insight into the mechanisms of multi-scale structure in the multi-phase complex systems in chemical engineering.展开更多
文摘针对灰狼优化(Grey wolf optimization,GWO)算法在处理复杂优化问题时优化精度不高,易陷于局部最优等问题,提出了一种强化狼群等级制度的灰狼优化(GWO based on strengthening the hierarchy of wolves,GWOSH)算法。该算法为灰狼个体设置了跟随狩猎和自主探索两种狩猎模式,并根据自身等级情况来控制选择狼群的狩猎模式。在跟随狩猎模式中,灰狼个体以等级高于自身的灰狼的位置信息来指引自己到达最优解区域;而在自主探索模式中,灰狼个体会同时审视等级高于自身的灰狼的位置信息和自身位置信息,并基于这些信息自主判断猎物的位置,同时两种更新模式都将引入优胜劣汰选择规则来确保种群的狩猎方向。对12个基准测试函数进行优化的结果表明:与已有的算法相比,GWOSH算法的全局搜索能力更强,更能有效避免易早熟收敛的问题,更适用于求解高维的复杂优化问题。
基金Project(70671040) supported by the National Natural Science Foundation of China
文摘In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and fine-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying the effectiveness and stronger global convergence ability of the EPSO.
基金Project(61471194)supported by the National Natural Science Foundation of ChinaProject(BK20140828)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars,Ministry of Education,China
文摘In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.
基金supported by the National Natural Science Foundation of China under Grant Nos.11688101,91634203,61304159by the National Center for Mathematics and Interdisciplinary Sciences
文摘This paper explores the application of noncooperative game theory together with the concept of Nash equilibrium to the investigation of some basic problems on multi-scale structure, especially the meso-scale structure in the multi-phase complex systems in chemical engineering. The basis of this work is the energy-minimization-multi-scale （EMMS） model proposed by Li and Kwauk （1994） and Li, et al. （2013） which identifies the multi-scale structure as a result of ＇compromise-in-competition between dominant mechanisms＇ and tries to solve a multi-objective optimization problem. However, the existing methods often integrate it into a problem of single objective optimization, which does not clearly reflect the ＇compromise-in-competition＇ mechanism and causes heavy computation burden as well as uncertainty in choosing suitable weighting factors. This paper will formulate the compromise in competition mechanism in EMMS model as a noncooperative game with constraints, and will describe the desired stable system state as a generalized Nash equilibrium. Then the authors will investigate the game theoretical approach for two typical systems in chemical engineering, the gas-solid fluidiza- tion （GSF） system and turbulent flow in pipe. Two different cases for generalized Nash equilibrinm in such systems will be well defined and distinguished. The generalize Nash equilibrium will be solved accurately for the GSF system and a feasible method will be given for turbulent flow in pipe. These results coincide with the existing computational results and show the feasibility of this approach, which overcomes the disadvantages of the existing methods and provides deep insight into the mechanisms of multi-scale structure in the multi-phase complex systems in chemical engineering.
