Adaptive global optimization with local search
Adaptive global optimization with local search
Computational Optimization and Applications
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Extensive Testing of a Hybrid Genetic Algorithm for Solving Quadratic Assignment Problems
Computational Optimization and Applications
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Adaptation for parallel memetic algorithm based on population entropy
Proceedings of the 8th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
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 large-scale 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 ability of the PMA-SLS to manage a more desirable diversity profile as the search progresses.