It’s all a treasure hunt

Imagine that you and your friends are looking for a treasure together. The treasure is magical, and it rewards not only the one who finds it, but also those near to it. Your group knows, approximately, where the treasure is, but not exactly sure of its definite location.

Your group then decided to split up with walkie-talkies and metal detectors. You use your walkie-talkie to inform everyone of your current position, and the metal detector to check your proximity to the treasure. In return, you gain knowledge of your friends’ positions, and also their distance from the treasure.

As a member of the group, you have two options:

  • Ignore your friends, and just search for the treasure the way you want it. Problem is, if you didn’t find it, and you’re far away from it, you get a very low reward.
  • Using the information you gather from your group, coordinate and find the treasure together. The best way is to know who is the one nearest to the treasure, and move towards that person.

Here, it is evident that by using the information you can gather from your friends, you can increase the chances of finding the treasure, and at the same time maximize the group’s reward. This is the basics of Particle Swarm Optimization (PSO). The group is called the swarm, you are a particle, and the treasure is the global optimum [CI2007].

Particle Swarm Optimization (PSO)

As with the treasure example, the idea of PSO is to emulate the social behaviour of birds and fishes by initializing a set of candidate solutions to search for an optima. Particles are scattered around the search-space, and they move around it to find the position of the optima. Each particle represents a candidate solution, and their movements are affected in a two-fold manner: (1) their cognitive desire to search individually, (2) and the collective action of the group or its neighbors. It is a fairly simple concept with profound applications.

One interesting characteristic of PSO is that it does not use the gradient of the function, thus, objective functions need not to be differentiable. Moreover, the basic PSO is astonishingly simple. Adding variants to the original implementation can help it adapt to more complicated problems.

The original PSO algorithm is attributed to Eberhart, Kennedy, and Shi [IJCNN1995] [ICEC2008]. Nowadays, a lot of variations in topology, search-space characteristic, constraints, objectives, are being researched upon to solve a variety of problems.

Why make PySwarms?

In one of my graduate courses during Masters, my professor asked us to implement PSO for training a neural network. It was, in all honesty, my first experience of implementing an algorithm from concept to code. I found the concept of PSO very endearing, primarily because it gives us an insight on the advantage of collaboration given a social situation.

When I revisited my course project, I realized that PSO, given enough variations, can be used to solve a lot of problems: from simple optimization, to robotics, and to job-shop scheduling. I then decided to build a research toolkit that can be extended by the community (us!) and be used by anyone.


  1. Engelbrecht, “An Introduction to Computational Intelligence,” John Wiley & Sons, 2007.
  1. Kennedy and R.C. Eberhart, “Particle Swarm Optimization,” Proceedings of the IEEE International Joint Conference on Neural Networks, 1995, pp. 1942-1948.
  1. Shi and R.C. Eberhart, “A modified particle swarm optimizer,” Proceedings of the IEEE International Conference on Evolutionary Computation, 1998, pp. 69-73.