Product design optimization using ant colony and bee algorithms: A comparison

Abstract

In recent years, heuristic algorithms, especially swarm intelligence algorithms, have become popular for product design, where problem formulations often are NP-hard (Socha and Dorigo, Eur J Oper Res 185:1155-1173, 2008). Swarm intelligence algorithms offer an alternative for large-scale problems to reach near-optimal solutions, without constraining the problem formulations immoderately (Albritton and McMullen, Eur J Oper Res 176:498-520 2007). In this paper, ant colony (Albritton and McMullen, Eur J Oper Res 176:498-520 2007) and bee colony algorithms (Karaboga and Basturk, J Glob Optim 39:459-471, 2007) are compared. Simulated conjoint data for different product design settings are used for this comparison, their generation uses a Monte Carlo design similar to the one applied in (Albritton and McMullen, Eur J Oper Res 176:498-520 2007). The purpose of the comparison is to provide an assistance, which algorithm should be applied in which product design setting. © Springer International Publishing Switzerland 2013.