A Comparison of Particle Swarm Optimization and the Genetic Algorithm
Massachusetts Institute of Technology
Abstract
Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. PSO is similar to the Genetic Algorithm (GA) in the sense that these two evolutionary heuristics are population-based search methods. In other words, PSO and the GA move from a set of points (population) to another set of points in a single iteration with likely improvement using a combination of deterministic and probabilistic rules. The GA and its many versions have been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly nonlinear, mixed…
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Authors
4Topics & keywords
- Particle swarm optimization
- Genetic algorithm
- Computer science
- Meta-optimization
- Multi-swarm optimization
- Mathematical optimization
- Metaheuristic
- Algorithm
- Industry, innovation and infrastructure