Analysing the performance of migrating birds optimisation approaches for large scale continuous problems
Paechter, B. (2016). Analysing the performance of migrating birds optimisation approaches for large scale continuous problems. In: (Ed.) Parallel Problem Solving from Nature -- PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings, , () ( ed.). (pp. 134-144). : . Springer International Publishing.
We present novel algorithmic schemes for dealing with large scale continuous problems. They are based on the recently proposed population-based meta-heuristics Migrating Birds Optimisation (MBO) and Multi-leader Migrating Birds Optimisation (MMBO), that have shown to be effective for solving combinatorial problems. The main objective of the current paper is twofold. First, we introduce a novel neighbour generating operator based on Differential Evolution (DE) that allows to produce new individuals in the continuous decision space starting from those belonging to the current population. Second, we evaluate the performance of MBO and MMBO by incorporating our novel operator to them. Hence, MBO and MMBO are enabled for solving continuous problems. Comparisons are carried out by applying both aforementioned schemes to a set of well-known large scale functions.
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The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.