What is Smart Optimization Algorithm 10

Swarm intelligence optimization algorithm is a kind of probability-based random search evolutionary algorithm, and the structure, research content, and calculation methods of each algorithm have great similarities. Therefore, the swarm intelligence optimization algorithm can establish a basic theoretical framework.

Mode: Step 1: Set the parameters and initialize the population;

Step 2: Generate a set of solutions and calculate their adaptation values;

step3: The individual is the most adaptable, and the optimal adaptation value of the group is obtained through comparison;

Step 4: Determine whether the termination conditions are met? If satisfied, end the iteration; Otherwise, turn to step2;

The biggest difference between the swarm intelligence algorithms lies in the algorithm update rules, which are based on the update of the movement step size of simulated social organisms (such as PSO, AFSA and SFLA), and there are also update rules set according to a certain algorithm mechanism (such as ACO).

Intelligent optimization algorithm is a heuristic optimization algorithm, including genetic algorithm, ant colony algorithm, tabu search algorithm, simulated annealing algorithm, particle swarm algorithm, etc. Intelligent optimization algorithms are generally designed for specific problems, with weak theoretical requirements and strong technicality. In general, we compare intelligent algorithms with optimization algorithms, which are fast and highly applicable.

It is a method of optimizing by using programs to simulate the evolutionary methods known in nature, such as genetic algorithms that simulate biological evolution, simulate natural selection for screening, and gradually return to the maximum.

Optimization algorithm refers to the optimization of the performance of the algorithm, such as:Time complexity, spatial complexity, correctness,Robustness

The era of big data.

When it comes, the algorithm has to process the data by an order of magnitude.

It is also getting bigger and bigger, and the scenarios for dealing with problems are ever-changing. In order to enhance the ability of the algorithm to deal with the problem, it is essential to optimize the sail holding algorithm. Algorithm optimization is generally the optimization of algorithm structure and convergence.

The same problem can be solved by different algorithms, and the quality of one algorithm will affect the efficiency of the algorithm and even the segment troublemaker. The purpose of algorithm analysis is to select suitable algorithms and improve them. The evaluation of an algorithm is mainly considered from the time complexity and space complexity.

Genetic algorithms

Genetic algorithms are also inspired by the natural sciences. This type of algorithm works by first generating a random set of solutions, called populations. At each step in the optimization process, the algorithm calculates the cost function of the entire population to obtain a ranking of the solutions, after which a new population is created --- called the next generation.

First of all, we add the solution at the top of the current population to the new population, which is called the meritocracy. The remainder of the new population is made up of a completely new solution formed by modifying the optimal solution.

There are two commonly used ways to modify a solution. One of these is called mutation, which is a small, simple, random change to an existing solution; Another way to modify a solution is called crossing or pairing, which is to take two solutions of the optimal type of solution and combine them in some way. After that, this process is repeated until the specified number of iterations is reached, or when the solution does not improve after several generations.