Multilevel Techniques for solving the Satisfiability Problem: Enhancing Tabu Search and Learning Automata

There are many complex problems in computer science that occur in knowledge-representation, artificial learning, Very Large Scale Integration (VLSI) design, security protocols and other areas. These complex problems may be deduced into satisfiability problems where the Boolean Satisfiability Problem (SAT) may be applied. This deduction is made in order to simplify complex problems into a specific propositional logic problem. The SAT problem is the most well-known nondeterministic polynomial time (NP) complete problem in computer science. In this book, we solve the SAT problem using a clustering technique - Multilevel - combined first with the Tabu Search algorithm and combined thereafter with finite Learning Automata. Tabu Search and finite Learning Automata are two very efficient approaches that have been used to solve SAT.