A binary tree seed algorithm with selection-based local search mechanism for huge-sized optimization problems

Abstract

Tree seed algorithm (TSA) is a recently proposed metaheuristic algorithm for solving continuous optimization problems. In order to use TSA in binary optimization problems, the SimLogicTSA method was developed by adding logic gates and Jaccard’s similarity measure to this algorithm by Cinar and Kiran. Although SimLogicTSA is generally successful in small, medium, and large size problems, it has not been successful in the huge-sized problems by stucking into local minima. To overcome this problem, a new local search mechanism called enhanced local search module (ELSM) is proposed and the SimLogicTSA-ELSM algorithm is suggested by implementing the ELSM mechanism to the original SimLogicTSA algorithm. The proposed ELSM mechanism consists of a swap operator and logic-based gates. To analyze the contribution of the ELSM mechanism to the algorithm, firstly, the original SimLogicTSA and SimLogicTSA-ELSM algorithms were compared on the Cap and M* problem sets. The obtained results showed that the proposed algorithm produced more successful results than the original SimLogicTSA. Then, the proposed SimLogicTSA-ELSM is compared with many state-of-art algorithms in the literature by using different performance metrics on Cap and M* problem sets. The results show that SimLogicTSA-ELSM outperforms the compared algorithms in nearly all cases. Especially, the performance of the SimLogicTSA-ELSM stands out in huge-sized problems.