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Position

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A particle's current point in the search space representing its candidate solution.

A position is a particle’s current point in the feasible search space, representing its candidate solution. In particle swarm optimization, particles move through this space while following successful neighboring solutions. section 9.2

Within PSO, position serves three practical roles:

  • Initialization gives each particle a current position before fitness evaluation begins. section 9.2
  • Updating adds the newly calculated velocity displacement to the current position: x(k + 1) = x(k) + v(k + 1) × 1. section 9.2.1
  • Each coordinate represents a decision dimension, such as one variable in a univariate problem or two in a bivariate problem. section 9.2.1

9.1

Use the motion-equation material to connect a particle’s current position with its velocity-based movement. section 9.2.1

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines position, identifies its dimensions, and explains its iterative update from velocity without developing deeper search-space analysis.
Examples
Uses univariate and bivariate optimization examples plus a two-dimensional Cartesian visualization of particle motion.
What you can do after
Apply the PSO motion equation to update a particle’s candidate position from its current position and new velocity.
How it connects
Connects position with particle swarm optimization, velocity, fitness evaluation, personal best, and global best.
Cover of Optimization Algorithms: AI techniques for design, planning, and control problems
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