Scheduling Functions for Position Updating in Population Based Optimization AlgorithmsJeremy Mange* and Sara Pace
US Army - TARDEC, Computational Methods and System Behavior, Warren, MI, USA
- Corresponding Author:
- Jeremy Mange
TARDEC, Computational Methods and System Behavior
Warren, MI, USA
E-mail: [email protected]
Received date: April 09, 2016; Accepted date: April 18, 2016; Published date: April 22, 2016
Citation: Mange J, Pace S (2016) Scheduling Functions for Position Updating in Population Based Optimization Algorithms. Int J Swarm Intel Evol Comput 5:133.doi:10.4172/2090-4908.1000133
Copyright: © 2016 Mange J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In many population-based optimization algorithms (Evolutionary Algorithms, Particle Swarm Optimization, etc.), each iteration of the algorithm involves a procedure-specific set of operations for each population member, followed by a resulting update of the position of that member within the problem search space. However, for algorithms in which these operations involve only a single population member and not the population as a whole, there is no inherent need to update every member at every iteration. In this paper, we propose a generalization of this updating procedure wherein a “scheduling” function is defined to dictate the ordering of updates through the application of algorithm, thus considering the typical procedure of updating every population member at every iteration as a particular “round-robin" schedule. Using the standard Particle Swarm Optimization algorithm (SPSO-2011) as a basis for demonstrating the concept, we compare a number of different scheduling functions and show that several of these functions outperform the typical round-robin schedule for a set of benchmark optimization problems.