Integrating tabu search and VLSN search to develop enhanced algorithms: A case study using bipartite boolean quadratic programs

• Published in: European journal of operational research Vol. 241; no. 3; pp. 697 - 707
• Main Authors: Glover, Fred, Ye, Tao, Punnen, Abraham P., Kochenberger, Gary
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.03.2015; Elsevier Sequoia S.A
• Subjects: Algorithms; Benchmarking; Boolean; Boolean algebra; Boolean variables; Complexity theory; Heuristic; Job shops; Mathematical models; Mathematical problems; Metaheuristics; Operational research; Optimization algorithms; Quadratic programming; Searching; Studies; Tabu search; Very large scale; Worst-case analysis; Worst-case analysis; Tabu search; Quadratic programming; Metaheuristics; Boolean variables
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2014.09.036

•We develop efficient heuristic algorithms based on tabu search, VLSN search, and a hybrid algorithm that integrates the two.•The computational study establishes that effective integration of simple tabu search with VLSN search results in superior outcomes.•We obtain solutions better than the best previously known for almost all medium and large size benchmark instances.•Landscape analysis of benchmark instances is given and this offers additional insights into the structure of these problems. The bipartite boolean quadratic programming problem (BBQP) is a generalization of the well studied boolean quadratic programming problem. The model has a variety of real life applications; however, empirical studies of the model are not available in the literature, except in a few isolated instances. In this paper, we develop efficient heuristic algorithms based on tabu search, very large scale neighborhood (VLSN) search, and a hybrid algorithm that integrates the two. The computational study establishes that effective integration of simple tabu search with VLSN search results in superior outcomes, and suggests the value of such an integration in other settings. Complexity analysis and implementation details are provided along with conclusions drawn from experimental analysis. In addition, we obtain solutions better than the best previously known for almost all medium and large size benchmark instances.