Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artifici...Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.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
Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a ...Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.展开更多
This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have ...This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.展开更多
This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear c...This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.展开更多
文摘Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.
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Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.
文摘This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine(TBM)in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang-Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength(UCS),Brazilian tensile strength(BTS),rock mass rating(RMR),rock quality designation(RQD),quartz content(q)and weathered zone as well as machine specifications including thrust force and revolution per minute(RPM)were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network(ANN)combined with both imperialist competitive algorithm(ICA)and particle swarm optimization(PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination(R^2),root mean square error(RMSE)and variance account for(VAF)were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior.
文摘This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.
