1. Particle Swarm Optimization
Particle Swarm Optimization (PSO), also known as PSO, is a new evolutionary algorithm developed in recent years.
Evolutionary algorithm. PSO algorithm is an evolutionary algorithm, similar to genetic algorithm. It also starts from the random solution and finds the optimal solution through iteration. It also evaluates the quality of the solution through fitness, but it is simpler than genetic algorithm. It does not have crossover and mutation operations of genetic algorithm, but seeks the global optimal value by following the current optimal value. The 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 various optimization problems, people put forward many optimization algorithms, such as mountain climbing and genetic algorithm. There are two main problems in the optimization problem: one is to find the global minimum, and the other is to have a high convergence speed. Mountain climbing method has high accuracy, but it is easy to fall into local minima. Genetic algorithm belongs to evolutionary algorithm.
(EvolutionaryAlgorithms), which imitates the mechanism of natural selection and heredity to find the optimal solution. Genetic algorithm has three basic operators: selection, crossover and mutation. However, the programming implementation of genetic algorithm is complicated, so it is necessary to encode the problem first and find the optimal solution before decoding it. The realization 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. 1995, Dr. Aberhart and Dr. Kennedy proposed a new algorithm; Particle swarm optimization algorithm. The 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) is a new evolutionary algorithm developed in recent years. PSO algorithm is an evolutionary algorithm, similar to genetic algorithm, which starts from random solution and finds the optimal solution through iteration. It also evaluates the quality of the solution through fitness, but the rules are simpler than those of genetic algorithm, and there is no crossover and mutation operation of genetic algorithm. It finds the global optimal value by following the optimal value of the current search.
Second, the genetic algorithm
Genetic algorithm is an evolutionary algorithm for solving optimization problems in computational mathematics. Evolutionary algorithm originally developed from some phenomena in evolutionary biology, including heredity, mutation, natural selection and hybridization. Genetic algorithms are usually implemented as simulations. For optimization problems, a population of abstract representations (called chromosomes) of a certain number of candidate solutions (called individuals) evolves to a better solution. Traditionally, notation is used (that is, strings of 0 and 1), but other notation can also be used. Evolution begins with a completely random population of 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.