Single-objective problem solving based on hybrid particle swarm optimization and gravity search algorithm (MATLAB code)

Introduction:
In optimization problems, single-objective problems refer to finding the optimal solution to an objective function. Hybrid particle swarm optimization (PSO) and gravitational search algorithm (GSA) are two commonly used optimization algorithms. They find optimal solutions by simulating the behavior of bird flocks and the gravitational interaction between objects respectively. This article will introduce how to use MATLAB to solve single-objective problems based on hybrid particle swarm and gravity search algorithms.

Algorithm principle:

1. Hybrid particle swarm optimization algorithm (PSO):

   • Initialize the particle's position and velocity.
   • Calculate the fitness value of each particle.
   • Updates the particle's velocity and position.
   • Update the global optimal solution.
   • Repeat the above steps until the stopping condition is reached.

2. Gravity Search Algorithm (GSA):

   • Initialize the object's position and mass.
   • Calculate the gravitational force and displacement on each object.
   • Update the object's position.
   • Update the global optimal solution.
   • Repeat the above steps until the stopping condition is reached.

MATLAB code implementation:
The following is the code that uses MATLAB to implement single-objective problem solving based on hybrid particle swarm optimization and gravitational search algorithms.

% 参数设置
max_iter = 100;  % 最大迭代次数
pop_size = 50