Abstract
Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the objective of intelligently combining heuristic methods to solve hard optimization problems. Ant colony optimization (ACO) algorithms have been proven to deal with Dynamic Optimization Problems (DOPs) properly. Despite the good results obtained by the integration of local search operators with ACO, little has been done to tackle DOPs. In this research, one of the most reliable ACO schemes, the MAX-MIN Ant System (MMAS), has been integrated with advanced and effective local search operators, resulting in an innovative hyper-heuristic. The local search operators are the Lin–Kernighan (LK) and the Unstringing and Stringing (US) heuristics, and they were intelligently chosen to improve the solution obtained by ACO. The proposed method aims to combine the adaptation capabilities of ACO for DOPs and the good performance of the local search operators chosen in an adaptive way and based on their performance, creating in this way a hyper-heuristic. The travelling salesman problem (TSP) was the base problem to generate both symmetric and asymmetric dynamic test cases. Experiments have shown that the MMAS provides good initial solutions to the local search operators and the hyper-heuristic creates a robust and effective method for the vast majority of test cases.
Highlights
Hyper-heuristics comprise a set of approaches with the common goal of automating design and tuning heuristic methods to solve hard computational search problems
We investigate two powerful local search heuristics designed for the travelling salesman problem (TSP), LK, and Unstringing and Stringing (US) heuristics that were adapted to cope with both symmetric and asymmetric cases; we describe them in Sections 3.2.1 and 3.2.2, respectively
We investigate the proposed hyper-heuristic, which cleverly combines the flexibility of Ant colony optimization (ACO) to provide solutions for symmetric and asymmetric dynamic changes with the power of improvement of two good local search operators
Summary
Hyper-heuristics comprise a set of approaches with the common goal of automating design and tuning heuristic methods to solve hard computational search problems. Their main goal is to produce applicable search methodologies more broadly. Ant colony optimization (ACO) algorithms have proved that they are capable to find the optimal (or near optimal) solution for difficult combinatorial optimization problems (e.g., the static travelling salesman problem (STSP) [3]). The STSP has been studied extensively for the last few decades [4] and it is one of the most challenging N P -complete combinatorial optimization problems. Literature publications have dealt only with static problems, without dynamic changes, i.e., the instances do not change during the problem solving
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