Jakšić Kruger, Tatjana
|Title:||Convergence analysis of swarm intelligence metaheuristic methods||Journal:||Communications in Computer and Information Science||Volume:||871||First page:||251||Last page:||266||Conference:||7th International Conference on Optimization Problems and Their Applications, OPTA 2018; Omsk; Russian Federation; 8 June 2018 through 14 June 2018||Issue Date:||1-Jan-2018||ISBN:||978-3-319-93799-1||ISSN:||1865-0929||DOI:||10.1007/978-3-319-93800-4_20||Abstract:||
Intensive applications and success of metaheuristics in practice have initiated research on their theoretical analysis. Due to the unknown quality of reported solution(s) and the inherently stochastic nature of metaheuristics, the theoretical analysis of their asymptotic convergence towards a global optimum is mainly conducted by means of probability theory. In this paper, we show that principles developed for the theoretical analysis of Bee Colony Optimization metaheuristic hold for swarm intelligence based metaheuristics: they need to implement learning mechanisms in order to properly adapt the probability rule for modification of a candidate solution. We propose selection schemes that a swarm intelligence based metaheuristic needs to incorporate in order to assure the so-called model convergence.
|Keywords:||Asymptotic properties | Nature-inspired methods | Optimization problems | Solution quality | Stochastic processes||Publisher:||Springer Link||Project:||Graph theory and mathematical programming with applications in chemistry and computer science
Serbian Ministry of Education, Science and Technological Development, Grant no. F159
Development of new information and communication technologies, based on advanced mathematical methods, with applications in medicine, telecommunications, power systems, protection of national heritage and education
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