Application of gravity search algorithm in extreme learning machine (GSA-ELM) regression prediction and MATLAB code implementation
Introduction:
Gravitational Search Algorithm (GSA) is an optimization algorithm that simulates the natural phenomenon of interaction between gravity and mass. It simulates the interaction between gravity and mass to find the optimal solution by optimizing the search space of the problem. Extreme Learning Machine (ELM) is a single-layer forward neural network that achieves rapid training and prediction by randomly generating a weight matrix between the input layer and the output layer. This article will introduce the application of gravity search algorithm in extreme learning machine regression prediction, and provide MATLAB code implementation.
Gravity Search Algorithm (GSA):
The Gravity Search Algorithm is a simulation optimization algorithm based on the interaction between physical gravity and mass. The basic idea of the algorithm is to regard the solution in the search space as an object with mass, and realize the search for the optimal solution through the interaction between gravity and mass. The main steps of the GSA algorithm are as follows:
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initialization:
- Set the number of search space solutions.
- Randomly initialize the position and mass of each solution.
- Set other algorithm parameters, such as gravitational constant, acceleration, etc.
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Gravity calculation:
- Calculates gravity based on the solution's position and mass.
- A more massive solution has a greater gravitational pull.
- Closer solutions have greater gravitational pull.
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Speed update:
- Calculate the velocity of each solution based on gravity.
- Velocity updates are calculated based on current velocity, gravity, and other parameters.
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Location update:
- The position of each solution is updated based on the velocity.
- The updated position will be used for the next iteration of gravity calculations.
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Convergence criterion:
- According to advance