Particle swarm optimization can be viewed as a system with two populations: a population of current positions and a population of personal best attractors.

In genetic algorithms, crossover is applied after selection – the goal is to create a new offspring solution using components from the best available solutions.

This paper describes evolutionary optimization algorithm that is based on particle swarm but with addition of crossover which is one of the evolution operators ...

Particle swarm optimization can be viewed as a system with two populations: a population of current positions and a population of personal best attractors.

A particle swarm optimization algorithm with crossover operation (PSOCO) is proposed. In the proposed PSOCO, two different crossover operations are employed.

PSO algorithms maintain a swarm of particles, where each particle represents a candidate solution to the optimization problem. Each particle position is adapted.

A variant of diversity guided Particle Swarm Optimization (PSO) algorithm named QIPSO for solving global optimization problems by including a crossover ...

This study proposes a multi-objective optimization algorithm to solve the routing problem in the urban multi-modal network.

The crossover is performed between each particle's individual best positions. After the crossover, the fitness of the individual best position is compared ...

Sep 13, 2022 · In this paper, we propose an optimization method to find a continuous two-class income distribution, which is able to delimit the boundaries of the two ...