粒子群算法优化支持向量机英文

粒子群算法优化支持向量机的英文表达为"Particle Swarm Optimization (PSO) for optimizing Support Vector Machines (SVM)". \[1\] PSO is a population-based stochastic optimization technique that is similar to other evolutionary computation (EC) techniques such as Genetic Algorithms (GA). These techniques are based on population and utilize a fitness function to evaluate the individuals in the population. They all update the population and search for the optimal solution using random techniques. However, unlike EC and GA techniques, Particle Swarm Optimization does not have genetic operators such as crossover and mutation. Instead, particles are updated based on their internal velocities. Additionally, the information sharing mechanism in Particle Swarm Optimization is different from other EC algorithms. In EC, chromosomes share information with each other, so the entire population moves towards an optimal region as a collective. But in PSO, only the best particle shares information with others. Particle Swarm Optimization is a highly effective algorithm for solving real-valued global optimization problems and is suitable for large-scale research. \[1\] PSO is inspired by the collective behavior of insects, herds, flocks, and schools, where these groups cooperate to search for food and each member of the group continuously changes its search pattern based on its own experience and the experience of other members. \[2\] The PSO algorithm determines the optimal values of FAMNN parameters using Particle Swarm Optimization, which was first proposed by Kennedy and Eberhart in 1995. \[3\] The algorithm is an evolutionary technique that draws inspiration from the social behavior of bird flocks or fish schools and simulates the properties of particles in a swarm. Each particle in the PSO system changes its position (state) over time. In the PSO system, particles fly around a multidimensional search space. During the flight, each particle adjusts its position based on its own experience and the best positions encountered by itself and its neighbors. In this algorithm, each particle has a velocity and a position. \[3\]
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