A partition-based convergence framework for population-based optimization algorithms

Population-based optimization algorithms, such as genetic algorithm and particle swarm optimization, have become a class of important algorithms for solving global optimization problems. However, there is an issue that the global convergence is often absent for most of them. This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve this troubling problem. In this framework, regular partitions and evolutions of populations are implemented alternatively. Specifically, the initial population is generated from a regular partition on the search space; after several generations of evolution of the population, the evolution result is returned to join in the regular partition again, and a new population is generated. Repeat such progress until some stop condition is satisfied. Global convergence is guaranteed for the framework. Then this convergence framework is applied to particle swarm optimization, differential evolution, and genetic algorithm. The modified algorithms are globally convergent and perform better than the original version.

Automated summary

This paper proposes a partition-based convergence framework for population-based optimization algorithms to solve the issue of global convergence. The framework alternates between regular partitions and evolutions of populations, ensuring global convergence. The framework is applied to particle swarm optimization, differential evolution, and genetic algorithm, resulting in improved global convergence and performance compared to the original versions.