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An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Binary Quadratic Programming

Unconstrained binary quadratic programming (UBQP) problem plays an important role in operational research due to its application potential and its computational challenge. This paper presents a new hybrid algorithm based on Harmony Search (HS) and Teaching-Learning-Based Optimization. The main features of the proposed algorithm called harmony search with teaching-learning (HSTL) are the integration of teaching-learning strategy in the basic harmony search. This hybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the Teaching-Learning-Based Optimization. Experiments on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.