AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The ant colony optimization meta-heuristic
New ideas in optimization
Tabu Search
A Metaheuristic for the Pickup and Delivery Problem with Time Windows
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
A GA(TS) Hybrid Algorithm for Scheduling in Computational Grids
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
Adaptive guidance of the search process in evolutionary optimization
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
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While meta-heuristics are effective for solving large-scale combinatorial optimization problems, they result from time-consuming trial-and-error algorithm design tailored to specific problems. For this reason, a software tool for rapid prototyping of algorithms would save considerable resources. This paper presents a generic software framework that reduces development time through abstract classes and software reuse, and more importantly, aids design with support of our user-defined strategies and hybridization of meta-heuristics. Most interestingly, we propose a novel way of redefining hybridization with the use of the "request and response" metaphor, which form an abstract concept for hybridization. Different hybridization schemes can now be formed with minimal coding, which gives our proposed Meta-heuristics Development Framework its uniqueness. To illustrate the concept, we restrict to two popular meta-heuristics Ant Colony Optimization and Tabu Search, and demonstrate MDF through the implementation of various hybridized models to solve the Traveling Salesman Problem.