Function Optimization Analysis Based on Gravitational Search Algorithm
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Resource Overview
The Gravitational Search Algorithm (GSA) is a novel heuristic optimization algorithm proposed by Esmat Rashedi and colleagues at Kerman University, Iran, in 2009. Inspired by the simulation of gravitational forces in physics, it belongs to the category of swarm intelligence optimization algorithms. GSA operates by treating search particles as celestial bodies moving through space, where gravitational interactions guide their motion according to dynamical laws. Particles with higher fitness values possess greater inertial mass, and gravitational forces drive all particles toward the heaviest mass, thus gradually converging to the optimal solution. In implementation, the algorithm calculates masses based on fitness values, updates velocities using gravitational forces, and iteratively refines particle positions. GSA demonstrates strong global search capabilities and rapid convergence for complex optimization problems.
Detailed Documentation
The Gravitational Search Algorithm (GSA) referenced in this text is a heuristic optimization algorithm introduced by Esmat Rashedi et al. from Kerman University, Iran, in 2009. Its design draws inspiration from simulating gravitational interactions in physics to develop a swarm intelligence-based optimization technique. In GSA, search particles are modeled as celestial objects moving through space, with gravitational forces governing their mutual attraction and motion dynamics. Particles exhibiting higher fitness values correspond to larger inertial masses, causing gravitational pull to steer all particles toward the mass-dominated region, thereby progressively approaching the optimal solution for optimization problems. The algorithm's implementation involves key steps: fitness evaluation to determine particle masses, force calculation using Newton's gravitational law, acceleration computation, and velocity/position updates through numerical integration. GSA is renowned for its robust global exploration capability and fast convergence rates, making it effective for solving multimodal and high-dimensional optimization challenges.
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