The patent belongs to the person who applied for the patent. Let's take a look at the artificial intelligence algorithms:
First, Particle Swarm Optimization
Particle swarm optimization, also known as particle swarm optimization, abbreviated as PSO, is a new evolutionary algorithm developed in recent years.
(（Evolu2tionary Algorithm - EA）。 PSO algorithm is a kind of evolutionary algorithm, which is similar to genetic algorithm. It also starts from random solution and looks for the optimal solution through iteration. It also evaluates the quality of the solution through fitness, but it is simpler than genetic algorithm. It has no Crossover and Mutation operation of genetic algorithm, and it looks for the global optimal value by following the optimal value currently searched. This algorithm has attracted the attention of academic circles because of its advantages of easy implementation, high precision and fast convergence, and has shown its superiority in solving practical problems.
Optimization is a common problem in industrial design, and many problems can be summed up in the end. In order to solve all kinds of optimization problems, many optimization algorithms have been proposed, such as hill climbing method and genetic algorithm. The optimization problem has two main problems: one is to find the global minimum, and the other is to have a high convergence speed. The hill climbing method has high accuracy, but it is easy to fall into local minimum. Genetic algorithm belongs to evolutionary algorithm.
(EvolutionaryAlgorithms), which seeks the optimal solution by imitating the mechanism of natural selection and heredity. Genetic algorithm has three basic operators: selection, crossover and mutation. However, the programming implementation of genetic algorithm is complicated. First, the problem needs to be coded, and after finding the optimal solution, it needs to be decoded. The implementation of the other three operators also has many parameters, such as crossover rate and mutation rate. Moreover, the selection of these parameters seriously affects the quality of the solution, but at present, the selection of these parameters mostly depends on experience. In 1995, Dr. Eberhart and Dr. kennedy proposed a new algorithm; Particle Swarm Optimization (PSO) algorithm. This algorithm has attracted the attention of academic circles because of its advantages of easy implementation, high precision and fast convergence, and has shown its superiority in solving practical problems.
particle swarm optimization (PSO) algorithm is a new evolutionary algorithm (evolu2tionaryalgorithm-ea) developed in recent years. PSO algorithm is a kind of evolutionary algorithm, and similar to genetic algorithm, it also starts from random solution and finds the optimal solution through iteration. It also evaluates the quality of the solution through fitness, but it is simpler than the rules of genetic algorithm, and it does not have Crossover and Mutation operations of genetic algorithm. It seeks the global optimum by following the currently searched optimal value.
second, genetic algorithm
genetic algorithm is used to solve optimization in computational mathematics, and it is a kind of evolutionary algorithm. Evolutionary algorithm was originally developed from some phenomena in evolutionary biology, including heredity, mutation, natural selection and hybridization. Genetic algorithm is usually implemented as a simulation. For an optimization problem, a population of abstract representations (called chromosomes) of a certain number of candidate solutions (called individuals) evolves to a better solution. Traditionally, the expression (that is, the string of and 1) is used, but other expressions can also be used. Evolution begins with a population of completely random individuals, and then occurs from generation to generation. In each generation, the fitness of the whole population is evaluated, and several individuals are randomly selected from the current population (based on their fitness), and a new life population is generated through natural selection and mutation, which will become the current population in the next iteration of the algorithm.