The present scenario in the design of machine elements includes the minimization of weight of the individual components in order to reduce the overall weight of the machine elements. It saves both cost and energy invo...
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ISBN:
(纸本)9783319037530;9783319037523
The present scenario in the design of machine elements includes the minimization of weight of the individual components in order to reduce the overall weight of the machine elements. It saves both cost and energy involved. Belts are used to transmit power from one shaft to another by means of pulleys which rotate at the same speed or different speeds. Generally, the weight of pulley acts on the shaft and bearings. In the present study, minimization of weight of a belt pulley system has been investigated. particle swarm optimization algorithm (PSO) is used to solve the above mentioned problem subjected to a set of practical constraints and it is compared with the results obtained by Differential Evolution algorithm (DEA). Our results indicate that PSO approach handles our problem efficiently in terms of precision and convergence and it outperforms the results presented in the literature.
This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway lines Minimizing the stopping times for all passenger trains, minimizing travel distance of empty ...
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ISBN:
(纸本)9781424447541
This paper develops a multi-objective optimization model for the passenger train stopping scheme on high-speed railway lines Minimizing the stopping times for all passenger trains, minimizing travel distance of empty trains and minimizing the number of transfer passengers are the three planning objectives of the model For a given travel demand and specified capacity of stops, the model is solved by heuristic algorithm An improved discrete particleswarmoptimization (PSO) algorithm is presented to determine the best-compromise train stopping scheme with high effectiveness and stability In the algorithm, a stop based representation is designed, and a new method is used to update the position and velocity of particles In order to keep the particleswarmalgorithm from premature stagnation, the simulated annealing algorithm, which has local search ability, is combined with the PSO algorithm to make elaborate search near the optimal solution, then the quality of solutions is improved effectively An empirical study on a given small railway network is conducted to demonstrate the effectiveness of the model and the performance of the algorithm The experimental results show that the hybrid algorithm has great advantages in both success rate and convergence speed compared with other discrete PSO algorithm and genetic algorithm, and an optimal set of stopping schemes can always be generated for a given demand To achieve the best planning outcome, the stopping schemes should be flexibly planned, and not constrained by specific ones as often set by the planner
This paper proposed discrete particleswarmoptimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding schem...
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ISBN:
(纸本)9783037854471
This paper proposed discrete particleswarmoptimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals. After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.
In a solar electric vehicle, the optimal sizing of hybrid power system can be considered as a multi-objective optimization problem. The two conflicting goals are to maximize the Loss of Peak Power Probability (LPPP) a...
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ISBN:
(纸本)9783037856932
In a solar electric vehicle, the optimal sizing of hybrid power system can be considered as a multi-objective optimization problem. The two conflicting goals are to maximize the Loss of Peak Power Probability (LPPP) and minimize the system cost. And the former is related to the reliability of the system while the latter relates to whether production prototype so the two optimization objectives are important. An improved particleswarmalgorithm was presented to optimal size the hybrid power system. Here the mutation operator of genetic algorithm was introduced and the acceleration factor could change with time. The optimization results show that: the improved particleswarmalgorithm can well solve the hybrid power system for multi-objective optimization problems.
With the large-scale access of distributed generation, the power flow characteristics of distribution network have been greatly changed. The conventional fault localization methods for distribution networks are no lon...
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ISBN:
(纸本)9798350375145;9798350375138
With the large-scale access of distributed generation, the power flow characteristics of distribution network have been greatly changed. The conventional fault localization methods for distribution networks are no longer applicable. To deal with the problems induced by large-scale access of distributed generation, a novel fault localization method based on improved particle swarm optimization algorithm is proposed in this paper. Aiming at the issues of potential misjudge, an objective function/ fitness value correction method is proposed to adapt to bidirectional power flow induced by distributed generation. Aiming at the issues of sluggish convergence and poor robustness in existing models, this paper introduces the Reverse-Local Learning based particle swarm optimization algorithm to improve the optimization efficiency and algorithm stability. Case study is performed on the IEEE-33 bus distribution network to demonstrate the accuracy and efficiency of the proposed fault localization method.
This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references f...
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ISBN:
(纸本)9783037855034
This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.
A hybrid particleswarmoptimization (HPSO) algorithm, which combines the advantages of Nelder-Mead simplex method (SM) and particleswarmoptimization (PSO) algorithm, is put forward to solve systems of nonlinear equ...
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ISBN:
(纸本)9781424448319
A hybrid particleswarmoptimization (HPSO) algorithm, which combines the advantages of Nelder-Mead simplex method (SM) and particleswarmoptimization (PSO) algorithm, is put forward to solve systems of nonlinear equations, and it can be used to overcome the difficulty in selecting good initial guess for SM and inaccuracy of PSO due to being easily trapped into local optimal. The algorithm has sufficiently displayed the performance of PSO's global searching and SM's accurate local search. Numerical computation results show that the approach has great robust, high convergence rate and precision, it can give satisfactory solutions of nonlinear equations.
Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment pr...
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ISBN:
(纸本)9782960053241
Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particleswarmoptimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partiel FJSP and Total FJSP, are tested. Computational results proved that the PSO developed is enough effective and efficient to solve flexible job-shop scheduling problem.
The paper presents two hybrid versions of the basic PSO algorithm, involving the use of the classical Grid Search (GS) algorithm and Design of Experiment (DOE) algorithm correspondingly. These hybrid versions have bee...
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ISBN:
(纸本)9781538656839
The paper presents two hybrid versions of the basic PSO algorithm, involving the use of the classical Grid Search (GS) algorithm and Design of Experiment (DOE) algorithm correspondingly. These hybrid versions have been applied to the problem of search of the parameters values of the SVM classifier. The results of experimental studies confirm the application efficiency of the hybrid versions of the basic PSO algorithm with the aim of reducing of the time expenditures for searching the optimum parameters of the SVM classifier while maintaining of high quality of its classification decisions.
The self-adaptive immune particleswarmoptimization (SAIPSO) algorithm is a hybrid algorithm based on immune algorithm and particle swarm optimization algorithm. SAIPSO algorithm has been implemented and achieved bet...
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ISBN:
(纸本)9783319925370;9783319925363
The self-adaptive immune particleswarmoptimization (SAIPSO) algorithm is a hybrid algorithm based on immune algorithm and particle swarm optimization algorithm. SAIPSO algorithm has been implemented and achieved better result compared with the classical particle swarm optimization algorithm. However, the theoretical support of the algorithm is equally important as the implementation of the algorithm. Therefore, this paper mainly uses the convergence theorem of random search algorithm and the mathematical induction to prove the convergence of SAIPSO algorithm, which will help the improvement and application of the algorithm in the future.
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