OEA_SAT: an organizational evolutionary algorithm for solving satisfiability problems

Authors:
Jing Liu;Wenrong Jiang;Weicai Zhong;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi, China;School of Computer and Information Technology, Shanghai Second Polytechnic University, Shanghai, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi, China
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

A novel evolutionary algorithm, Organizational Evolutionary Algorithm for SATisfiability problems (OEA_SAT), is proposed in this paper. OEA_SAT first divides a SAT problem into several sub-problems, and each organization is composed of a sub-problem. Thus, three new evolutionary operators, namely the self-learning operator, the annexing operator and the splitting operator are designed with the intrinsic properties of SAT problems in mind. Furthermore, all organizations are divided into two populations according to their fitness. One is called best-population, and the other is called non-best-population. The idea behind OEA_SAT is to solve the sub-problem first, and then synthesize the solution for the original problem by adjusting the variables which have conflicts. Since the dimensions of sub-problems are smaller and the sub-ones are easy to be solved compared with the original one, the computational cost is reduced in this way. In the experiments, 3700 benchmark SAT problems in SATLIB are used to test the performance of OEA_SAT. The number of variables of these problems is ranged from 20 to 250. Moreover, the performance of OEA_SAT is compared with those of two well-known algorithms, namely WalkSAT and RFEA2. All experimental results show that OEA_SAT has a higher success ratio and a lower computational cost. OEA_SAT can solve the problems with 250 variables and 1065 clauses by only 1.524 seconds and outperforms all the other algorithms.