Software Framework for Flexible User Defined Metaheuristic Hybridization

Metaheuristic algorithms have been widely used for solving Combinatorial Optimization Problem (COP) since the last decade. The algorithms can produce amazing results in solving complex real life problems such as scheduling, time tabling, routing and tasks allocation. We believe that many researchers will find COP methods useful to solve problems in many different domains. However, there are some technical hurdles such as the steep learning curve, the abundance and complexity of the algorithms, programming skill requirement and the lack of user friendly platform to be used for algorithm development. As new algorithms are being developed, there are also those that come in the form of hybridization of multiple existing algorithms. We reckon that there is also a need for an easy, flexible and effective development platform for user defined metaheuristic hybridization. In this article, a comparative study has been performed on several metaheuristics software frameworks. The result shows that available software frameworks are not adequately designed to enable users to easily develop hybridization algorithms. At the end of the article, we propose a framework design that will help bridge the gap. We foresee the potential of scripting language as an important element that will help improve existing software framework with regards to the ease of use, rapid algorithm design and development. Thus, our efforts are now directed towards the study and development of a new scripting language suitable for enhancing the capabilities of existing metaheuristic software framework.

Keywords

• optimization

• metaheuristic

• hybridization

• scripting language

• software framework