Abstract
The maximum clique problem is a classic graph optimization problem that is NP-hard even to approximate. For this and related reasons, it is a problem of considerable interest in theoretical computer science. The maximum clique also has several real-world applications. In this paper, an efficient algorithm for the maximum clique problem using improved Hopfield neural network is presented. In this algorithm, the internal dynamics of the Hopfield neural network is modified to efficiently increase exchange of information between neurons and permit temporary increases in the energy function in order to avoid local minima. The proposed algorithm is tested on two types of random graphs and DIMACS benchmark graphs. The simulation results show that the proposed algorithm is better than previous works for solving the maximum clique problem in terms of the computation time and the solution quality.
