Intelligent Computing/Computational intelligence/intelligent Algorithms

The intelligent computing method is the same as the calculation method (or numerical analysis), and calculates the approximate solution of satisfying real solution! Take this approximate solution instead of the real solution to use.
Usually, many problems are not analytic solution, at this time can be used to calculate the numerical solution, when the problem is complex, the calculation method is too large or difficult to use the calculation method of the algorithm implementation, you can consider the intelligent calculation Method!

In essence, intelligent computing methods are bionic, randomized, empirical, nature is random, empirical, extracted the nature of this feature, automatic adjustment of the formation of experience

Mainly include: Genetic algorithm GA (Genetic algorithm)---nature of the fittest ant colony algorithm aco (Ant Colony (Group) optimization)---ant foraging particle swarm optimization algorithm/bird swarm algorithm PSO (particle Swarm O ptimization)---Avian predator-prey immune algorithm AIS (Artificial immune system)---Human immune system fuzzy logic simulated annealing algorithm SAA (simulate anneal arithmetic)---annealing

Distribution estimation algorithm EDA (estimation distributed algorithm)

The Distributed Estimation algorithm (EDA) is a new stochastic optimization algorithm based on statistical principle. There are obvious differences between EDA and genetic Algorithm (GA). GA uses crossover and mutation to produce new individuals, and EDA predicts the best region of search by searching spatial sampling and statistical learning, and then produces excellent new individuals. Compared to GA based on the micro-level evolution of the gene, EDA employs a macro-level evolutionary approach based on search space, with stronger global search capability and faster convergence speed.

Tabu Search Algorithm TS (Tabu search)

is a modern heuristic algorithm, proposed by Professor Fred Glover of the University of Colorado in the United States around 1986, is a search method used to jump off local optimal solutions. Its first create an initialization scheme; Based on this, the algorithm "moves" to an adjacent scheme. After a lot of continuous moving process, improve the quality of the solution.

Neural network neural Networks culture algorithm CA (cultural algorithm)---cultural communication

The cultural algorithm simulates the evolutionary process of human social culture and establishes dynamic belief space in the evolutionary group space, which is used to guide the search of data. This paper introduces the principle and process of the culture algorithm, classifies the cultural algorithm according to the characteristics of the problem, and gives the solving steps of the different types of problems. According to the simulation results of the cultural algorithm, it is concluded that the algorithm not only converges fast, but also obtains better results, which shows that the cultural algorithm is an evolutionary algorithm of global optimization search and has a wide application prospect.

Memetic algorithm/culture gene algorithm

The cultural genetic algorithm (memetic algorithm, MA) is an optimization algorithm based on simulated cultural evolution proposed by Pablo Moscato [1], which is essentially a population-based global search and a combination of local heuristic search based on the individual.

Artificial Colony algorithm BA (Bees algorithm)

Intelligent Computing/Computational intelligence/intelligent Algorithms