An adaptive memetic algorithm for the architecture optimisation problem

Architecture design is one of the most important steps in software development, since design decisions affect the quality of the final system (e.g. reliability and performance). Due to the ever-growing complexity and size of software systems, deciding on the best design is a computationally intensive and complex task. This issue has been tackled by using optimisation method, such as local search and genetic algorithms. Genetic algorithms work well in rugged fitness landscapes, whereas local search methods are successful when the search space is smooth. The strengths of these two algorithms have been combined to create memetic algorithms, which have shown to be more efficient than genetic algorithms and local search on their own. A major point of concern with memetic algorithms is the likelihood of loosing the exploration capacity because of the ‘exploitative’ nature of local search. To address this issue, this work uses an adaptive scheme to control the local search application. The utilised scheme takes into account the diversity of the current population. Based on the diversity indicator, it decides whether to call local search or not. Experiments were conducted on the compo- nent deployment problem to evaluates the effectiveness of the proposed algorithm with and without the adaptive local search algorithm.