Similarity and Logic Gate-Based Tree-Seed Algorithms for Binary Optimization

This paper focuses on solving binary optimization problems by using Tree-Seed Algorithm, TSA for short. While TSA is firstly proposed for solving optimization problems with continuously-structured solution space, TSA is modified to solve binary optimization problems, which is a subfield of discrete optimization, by using logic gates (LogicTSA) and similarity measurement techniques (SimTSA). In order to improve performance of these methods, a hybrid variant (SimLogicTSA) is also proposed. The performance of the proposed algorithms is investigated on uncapacitated facility location problems (UFLPs), which are pure binary optimization problems. The experimental results on 15 test instances are compared with each other and state-of-art algorithms. The comparisons demonstrate that hybrid variant of the algorithm is better than the other variants of the algorithm and state-of-art algorithms in terms of solution quality and robustness.