Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Parallel simulated annealing algorithms
Journal of Parallel and Distributed Computing
Genetic, Iterated and Multistart Local Search for the Maximum Clique Problem
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
An effective local search for the maximum clique problem
Information Processing Letters
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
A new trust region technique for the maximum weight clique problem
Discrete Applied Mathematics - Special issue: International symposium on combinatorial optimization CO'02
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
An evolutionary algorithm with guided mutation for the maximum clique problem
IEEE Transactions on Evolutionary Computation
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The Cross Entropy algorithm is a new search method for combinatorial problem. However, it needs considerable computational time to achieve good solution quality. To make the Cross Entropy algorithm faster, this paper proposes a leader-based cooperative parallel algorithm. Unlike the widely used coarse-grained parallelization strategy, our method has a leader, which can move around freely, and several controlled followers. To evaluate the performance of the algorithm, we implement our algorithm using OpenMPI on MIMD architecture, and has applied it on 25 selected MCP benchmark problems. The speedup and efficiency is analyzed, and the results obtained are compared with those obtained by four other best heuristic algorithms, GLS, Edge-AC+LS, EA/G and RLS.