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Particle swarm optimization
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The multi-objective optimization algorithm based on particle swarm optimization is based on Pareto optimal solution discussion. Genetic algorithm has made notes. The following introduces the related concepts of Pareto optimal solution. This paper is a reading note based on references. ...
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Particle swarm optimization
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Matlab code for Pareto
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Pareto optimal solutions (Pareto efficiency) is an ideal State, assumes that inherent in a group of people and resources that can be allocated, the change from one assignment to another, in the absence of any situation of premise, make at least one change for the better. Pareto optimal States are no longer have more leeway, in other words, Pareto improvement is to achieve Pareto optimality of the paths and methods. Pareto optimum is between fairness and efficiency of the "ideal".
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Particle Swarm
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The purpose of penalty function is constrained problem into an unconstrained problem. Particle Swarm are limited in the scope of conditions within the cluster, namely searching in the range of feasible solutions.
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Particle swarm optimization 2
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PSO algorithm is to simulate the process of a group of birds looking for food. Each bird is a particle in PSO, which is the possible solution of the problem we need to solve. In the process of looking for food, these birds keep changing their position and speed in the air. You can also observe that in the process of searching for food, the birds are scattered at first, and gradually they gather into a group, which goes from high to low, from left to right, until they finally find food. This process turns into a mathematical problem
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Program code
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NSGA -II is one of the most popular multi-objective evolutionary algorithms, and it reduces the complexity of non-dominated sorting genetic algorithm, running fast, advantage of the convergence of the solution set, become a benchmark for other multiobjective optimization algorithms. NSG..
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Multi-objective
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Multi-objective Particle Swarm algorithm simulations of social behavior in animals, the process of finding an optimal design points compared to the foraging activity of these organisms. In other words, these examples in the design space to find the best location, an example of the best locations and along the current speed and inertia is used to determine the next adjacent element in the direction of the position of the particle.
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Take a stopwatch
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Multi-objective Particle Swarm Optimization Matlab code. Multi-objective Particle Swarm algorithm simulations of social behavior in animals, the process of finding an optimal design points compared to the foraging activity of these organisms. In other words, these examples looking for the best position in the design space, one of the best examples and along the direction of the current speed and inertia of zero elements are used to determine the position of the next particle
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PSO
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Optimal design theory is presented for the first time with the electronic computer technology, extensive application of finite element method and mathematical induction and development of engineering structural optimization of rapid development, and gradually become a foreign subject.
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Matlab code
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So-called agency model refers to not reduce the accuracy of the structural calculation of a small, short period, but results or physical experiment mathematical model for numerical analysis. It is the use of response response value for a class of model to predict the location point.
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