Abstract
Heuristic Search techniques are known for their efficiency and effectiveness in solving NP-Hard problems. However, there has been limited success so far in constructing a software toolkit which is dedicated to these methods and can fully support all the stages and aspects of researching and developing a system based on these techniques. Some of the reasons for that include the lack of problem modelling facilities and domain specific frameworks which specifically suit the operations of heuristic search, tedious code optimisations which are often required to achieve efficient implementations of these methods, and the large number of available algorithms - both local search and population-based - which make it difficult to implement and evaluate a range of techniques to find the most efficient one for the problem at hand. The iOpt Toolkit, presented in this article, attempts to address these issues by providing problem modelling facilities well-matched to heuristic search operations, a generic framework for developing scheduling applications, and a logically structured heuristic search framework allowing the synthesis and evaluation of a variety of algorithms. In addition to these, the toolkit incorporates interactive graphical components for the visualisation of problem and scheduling models, and also for monitoring the run-time behaviour and configuring the parameters of heuristic search algorithms.
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Voudouris, C., Dorne, R., Lesaint, D., Liret, A. (2001). iOpt: A Software Toolkit for Heuristic Search Methods. In: Walsh, T. (eds) Principles and Practice of Constraint Programming — CP 2001. CP 2001. Lecture Notes in Computer Science, vol 2239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45578-7_58
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DOI: https://doi.org/10.1007/3-540-45578-7_58
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