A Framework for Fast Proto-typing of Meta- heuristics Hybridization

Hybrids of meta-heuristics have been shown to be more effective and adaptable than their parents in solving various combinatorial optimization problems. However, hybridized schemes are more tedious to implement due to their complexity. We address this problem by proposing the Meta-heuristics Development Framework (MDF). In addition to being a framework that promotes reuse to reduce developmental effort, the key strength of MDF lies in its ability to model metaheuristics using a “Request, Sense and Response” (RSR) schema, which decomposes algorithms into a set of well-defined modules that can be flexibly assembled through an intelligent central controller. Under this scheme, hybrid schemes become an event-based search that can adaptively trigger a desired parent’s behavior in response to search events. MDF can hence be used to design and implement a wide spectrum of hybrids with various degrees of collaboration thereby offering the algorithm designer quick turnaround in designing and testing his meta-heuristics. This is illustrated in this paper through the construction of hybrid schemes using Ants Colony Optimization (ACO) and Tabu Search (TS).