Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 5:00 PM ET (12:00 - 21:00 UTC). We apologize for the inconvenience.
By Topic

A multiobjective ant colony-based optimization algorithm for the bin packing problem with load balancing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lara, O.D. ; Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA ; Labrador, M.A.

This paper presents ABLA, a novel multiobjective ant colony-based optimization algorithm to address the bin packing problem with load balancing. ABLA incorporates (1) a new probabilistic decision rule that builds solutions by making use of individual pheromone matrices for each objective function; (2) a new pheromone updating approach in which ants deposit variable amounts of pheromone; (3) two new local search methods to improve load balancing: LBH and LBH-AB; and (4) the Pareto dominance approach to select optimal solutions. ABLA is compared to a Multiobjective Max-Min Ant System (MO-MMAS) and an adapted multiobjective version of the First-Fit Decreasing (FFD) algorithm, which is the best known ρ-approximation algorithm for the bin packing problem. Results show that ABLA finds better solutions than both FFD and MO-MMAS, and that LBH and LBH-AB noticeably improve the load balancing across bins.

Published in:

Evolutionary Computation (CEC), 2010 IEEE Congress on

Date of Conference:

18-23 July 2010