Solving large scale combinatorial optimization using PMA-SLS

Memetic algorithms have become to gain increasingly important for solving large scale combinatorial optimization problems. Typically, the extent of the application of local searches in canonical memetic algorithm is based on the principle of “more is better”. In the same spirit, the island model parallel memetic algorithm (PMA) is an important extension of the canonical memetic algorithm which applies local searches to every transitional solutions being considered. For PMA which applies complete local search, we termed it as PMA-CLS. In this paper, we consider the island model PMA with selective application of local search (PMA-SLS) and demonstrate its utility in solving complex combinatorial optimization problems, in particular largescale quadratic assignment problems (QAPs). Based on our empirical results, the PMA-SLS compared to the PMA-CLS, can reduce the computational time spent significantly with little or no lost of solution quality. This we concluded is due mainly to the a...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Jing Tang, Meng-Hiot Lim, Yew-Soon Ong, Meng Joo Er
• Keywords: Canonical Memetic Algorithm, Combinatorial Optimization Problems, GECCO 2005, Memetic Algorithm