Implementation of digital pheromones for use in particle swarm optimization

Thumbnail Image
Foo, Jung
Winer, Eliot
Major Professor
Committee Member
Journal Title
Journal ISSN
Volume Title
Kalivarapu, Vijay
Teaching Professor
Research Projects
Organizational Units
Organizational Unit
Mechanical Engineering
The Department of Mechanical Engineering at Iowa State University is where innovation thrives and the impossible is made possible. This is where your passion for problem-solving and hands-on learning can make a real difference in our world. Whether you’re helping improve the environment, creating safer automobiles, or advancing medical technologies, and athletic performance, the Department of Mechanical Engineering gives you the tools and talent to blaze your own trail to an amazing career.
Journal Issue
Is Version Of

This paper presents a new approach to particle swarm optimization (PSO) using digital pheremones to coordinate the movements of the swarm within an n-dimensional design space. In traditional PSO, an initial randomly generated population swarm propagates towards the global optimum over a series of iterations. Each particle in the swarm explores the design space based on the information provided by previous best particles. This information is used to generate a velocity vector indicating a search direction towards a promising design point, and to update the particle positions. This paper presents how digital pheromones can be incorporated into the velocity vector update equation. Digital pheromones are models simulating the real pheromones produced by insects for communication to indicate a source of food or a nesting location. This principle of communication and organization between each insect in a swarm offers substantial improvement when integrated into PSO. Particle swarms search the design space with digital pheromones aiding communication within the swarm to improve search efficiency. Through additional information from the pheromones, particles within the swarm exploring the design space and locate the solution more efficiently and accurately than traditional PSO. In this paper, the development of this method is described in detail along with the results from several optimization test problems.


This is a conference proceeding from Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, (2006): AIAA 2006-1917, doi: 10.2514/6.2009-2192. Posted with permission.

Sun Jan 01 00:00:00 UTC 2006