In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineerin...In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.展开更多
In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were review...In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method (CPM) based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu- tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor- hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve lar^e-scale ~eneralized ventilation networks o^timization problem in the future.展开更多
A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm...A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.展开更多
S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameter...S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.展开更多
基金Supported by the National Basic Research Program of China (2012CB720500)the National Natural Science Foundation of China (60974008)
文摘In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.
基金financially supported by the National Natural Science Foundation of China (No. 51134023)
文摘In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method (CPM) based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu- tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor- hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve lar^e-scale ~eneralized ventilation networks o^timization problem in the future.
文摘A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.
基金Supported by the 863 Project under Grant No.2008AA092301the Fundamental Research Foundation of Harbin Engineering University under Grant No.2007001
文摘S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.