Essential Modifications on Biogeography-Based Optimization Algorithm

Abstract

Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications were employed to enhance its performance. This paper proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process after the mutation stage is sized to avoid any corruption on the suitability index variables. The obtained results through wide variety range of test functions with differentdimensions and complexities proved that the BBO performance can be enhanced effectively without using any complicated form of the immigration and emigration rates. This essential modification has to be considered as an initial step for any other modification.

Dual technique of reconfiguration and capacitor placement for distribution system

“…The BBO algorithm first introduced by Dan Simon in 2008 inspired from nature of geological distribution for living beings [21]. The original algorithm suffers from trapping in local optima and weakly behavior [22]. The MBBO algorithm introduced in this paper to solve these drawbacks, also it first time used for DSR and OCP problems and demonstrated promising results over their original version in terms of robustness and good performance.…”

Taxonomy of bio-inspired optimization algorithms

“…Applications that use these ideas allow information sharing between candidate solutions (Simon, 2008). In BBO, each habitat is considered as an individual and has its habitat suitability index (HSI) instead of fitness value to show the efficiency of individual (Alroomi, Albasri & Talaq, 2013). High-HSI habitat denotes a good solution and low-HSI habitat denotes a poor solution.…”

Multi-objective facility layout problems using BBO, NSBBO and NSGA-II metaheuristic algorithms

“…(12) respectively to start the migration operator. (Alroomi et al, 2013) Now the probability of presence of S species in the island is calculated, which is denoted by PS. This probability is obtained from Eq.…”

“…In island principle, the number of species present at equilibrium state can be differed due to some peripheral happenings such as diseases, tsunamis, volcanoes or earthquakes which cause decrease in total number of species. If there are other suitable events which provide good features to an island, they improve the solution (Alroomi et al, 2013). The mutation operation is used to increase the diversity of the population members to obtain the good solutions.…”

“…BBO is an evolutionary, population-based algorithm (Simon, 2008). This algorithm is based on the principle of island biogeography in which geographical distribution of biological species is explained (Alroomi et al, 2013). BBO algorithm follows migration and mutation operations to reach global minimum solution.…”