A machine program for theorem-proving
Communications of the ACM
Scatter Search with Random Walk Strategy for SAT and MAX-W-SAT Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Solving weighted Max-Sat optimization problems using a Taboo Scatter Search metaheuristic
Proceedings of the 2004 ACM symposium on Applied computing
An efficient solver for weighted Max-SAT
Journal of Global Optimization
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Cooperative bees swarm for solving the maximum weighted satisfiability problem
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Hi-index | 0.00 |
We introduce an advanced version of Bee Swarm Optimization metaheuristic (BSO) which is inspired from the foraging behavior of real bees. The objective of this work is to enhance the performances of BSO by subdividing the set of variables into groups covering disjointed sub-regions in the search space. To each sub-region is assigned a bee that performs a local search, and the search process is guided by the intensification and diversification principles. The subdivision of the set of variables is strongly dependent on the considered problem and aims at both reducing the execution time and maximizing the coverage of the search space. Our new approach called ABSO for Advanced Bees Swarm Optimization was applied to the weighted MAX-SAT and the comparison of experimental results showed that it outperforms the BSO algorithm.