Breakout local search for the Steiner tree problem with revenue, budget and hop constraints

• Published in: European journal of operational research Vol. 232; no. 1; pp. 209 - 220
• Main Authors: Fu, Zhang-Hua, Hao, Jin-Kao
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.01.2014; Elsevier Sequoia S.A; Elsevier
• Subjects: Adaptive perturbation; Algorithms; Benchmarks; Budgeting; Combinatorial analysis; Computer Science; Constrained combinatorial optimization; Decision trees; Diversification; Graphs; Heuristic; Heuristic search; Mathematical models; Mathematical problems; Network design; Optimization; Optimization algorithms; Revenues; Searching; Steiner tree problems; Studies; Trees; Network design; Adaptive perturbation; Heuristic search; Constrained combinatorial optimization; Steiner tree problems
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2013.06.048

•STPRBH is a generalization of the conventional Steiner tree problem.•We propose a heuristic algorithm based on the Breakout Local Search (BLS).•The proposed heuristic is assessed on 240 well known benchmark instances.•BLS finds improved results for 49 out of the 56 most challenging instances. The Steiner tree problem (STP) is one of the most popular combinatorial optimization problems with various practical applications. In this paper, we propose a Breakout Local Search (BLS) algorithm for an important generalization of the STP: the Steiner tree problem with revenue, budget and hop constraints (STPRBH), which consists of determining a subtree of a given undirected graph which maximizes the collected revenues, subject to both budget and hop constraints. Starting from a probabilistically constructed initial solution, BLS uses a Neighborhood Search (NS) procedure based on several specifically designed move operators for local optimization, and employs an adaptive diversification strategy to escape from local optima. The diversification mechanism is implemented by adaptive perturbations, guided by dedicated information of discovered high-quality solutions. Computational results based on 240 benchmarks show that BLS produces competitive results with respect to several previous approaches. For the 56 most challenging instances with unknown optimal results, BLS succeeds in improving 49 and matching one best known results within reasonable time. For the 184 instances which have been solved to optimality, BLS can also match 167 optimal results.