Parallel simulated annealing algorithms
Journal of Parallel and Distributed Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Ant Colony Optimization
Exploring e-learning knowledge through ontological memetic agents
IEEE Computational Intelligence Magazine
Neural Computing and Applications - Special Issue on WCCI2008
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We have recently proposed a Population-based Guided Local Search (P-GLS) framework for solving difficult combinatorial optimization problems. In P-GLS, several agents of guided local search (GLS) procedures are run in a parallel way. These agents exchange information acquired from their previous search to make their further search more rational. We suggested based on the well-known proximate optimality principle (POP) that the shared features between the current agents' local optimal solutions are more likely to be part of the best solution to the problem; therefore these features should not be penalized. However, sometimes some of these common features may not exhibit in a global optimal solution. In this paper, a new framework is proposed to improve the performance as well as overcome the limitations in P-GLS. It applies two new different penalization strategies that increase favouring common features based on their occurrences in the agents' local optimal solutions during the search. The performance of the new algorithm, examined on the Traveling Salesman Problem (TSP), is investigated and evaluated in terms of solution quality and the speed. The experimental results demonstrate that the new algorithm outperforms the parallel GLS algorithm without collaboration and other state-of-the-art algorithms.