A Time-Sensitive System for Black-Box Combinatorial Optimization

When faced with a combinatorial optimization problem, practitioners often turn to black-box search heuristics such as simulated annealing and genetic algorithms. In black-box optimization, the problem-specific components are limited to functions that (1) generate candidate solutions, and (2) evaluate the quality of a given solution. A primary reason for the popularity of black-box optimization is its ease of implementation. The basic simulated annealing search algorithm can be implemented in roughly 30-50 lines of any modern programming language, not counting the problem-specific local-move and cost-evaluation functions. This search algorithm is so simple that it is often rewritten from scratch for each new application rather than being reused. In this paper, we examine whether it pays to develop a more sophisticated, general-purpose heuristic optimization engine. The issue is whether a substantial performance improvement or ease-of-use gain results..