Solving dynamic optimisation problems with revolutionary algorithms. (1st January 2013)

• Record Type: Journal Article
• Authors: Hochreiter, Ronald; Waldhauser, Christoph
• Is Part Of: International journal of innovative computing and applications. Volume 5:Number 3(2013)
• Page Start: 143
• Page End: 151
• Publication Date: 2013-01-01
• Subjects: cultural algorithm -- dynamic optimisation -- genetic algorithm; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

The optimisation of dynamic problems is both widespread and difficult. When conducting dynamic optimisation, a balance between re-initialisation and computational expense has to be found. There are multiple approaches to this. In parallel genetic algorithms, multiple sub-populations concurrently try to optimise a potentially dynamic problem. But as the number of sub-population increases, their efficiency decreases. Cultural algorithms provide a framework that has the potential to make optimisations more efficient. But they adapt slowly to changing environments. We thus suggest a confluence of these approaches: revolutionary algorithms. These algorithms seek to extend the evolutionary and cultural aspects of the former two approaches with a notion of the political. By modelling how belief systems are changed by means of revolution, these algorithms provide a framework to model and optimise dynamic problems in an efficient fashion. The superiority of revolutionary algorithms over cultural and purely genetic algorithms is demonstrated in the solving of a standard dynamic facility location problem.