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[Editor] chapter13 DL : 0: 粒子群算法优化的详细讲解和实例应用，欢迎下载-Particle swarm optimization algorithm and explain in detail the application example, welcome to download
Date : 2025-12-27 Size : 718kb User : 高婷婷
[Editor] Files-and-Codes DL : 0: Improving the efficiency of three-phase squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the three.
Date : 2025-12-27 Size : 33kb User : behzad farhadi
[Editor] dcmotor DL : 0: DC MOTOR WITH MATLAB Improving the efficiency of three-phase squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the thr-DC MOTOR WITH MATLAB Improving the efficiency of three-phase squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the three
Date : 2025-12-27 Size : 10kb User : behzad farhadi
[Editor] Files-and-Codes DL : 0: SYSTEM DEGREE TWO WITH MATLAB . Improving the efficiency of three-phase squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the thr-SYSTEM DEGREE TWO WITH MATLAB . Improving the efficiency of three-phase squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the three
Date : 2025-12-27 Size : 13kb User : behzad farhadi
[Editor] sim33a DL : 0: electrical system with matlab. Improving the efficiency of three-phase squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the three book. Improving the efficiency of three-phase good squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the three THE ROOT.
Date : 2025-12-27 Size : 15kb User : behzad farhadi
[Editor] cir4trasformer DL : 0: paralell trans 3 phase with matlab. Improving the efficiency of three-phase squirrel cage induction motors, which are the most energy consuming electric machines in the world, saves much energy. The efficiency can be raised by optimizing the induction motor design. The objective of this paper is mainly to develop a modified particle swarm optimization (MPSO) technique to the three plus direct
Date : 2025-12-27 Size : 9kb User : behzad farhadi
[Editor] myMatlab-code DL : 0: In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search [3] and share some similarities with Estimation of Distribution
Date : 2025-12-27 Size : 14kb User : ibra
[Editor] ypea104-acor DL : 0: continuous ACO. In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search [3] and share some similarities with Estimation of Distribution
Date : 2025-12-27 Size : 7kb User : ibra
[Editor] PSO DL : 0: In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle s position and velocity. Each particle s movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.
Date : 2025-12-27 Size : 1kb User : ibra