%0 Journal Article %1 noauthororeditor %A Alroomi, Ali R. %A Albasri, Fadhel A. %A H.Talaq, Jawad %D 2013 %J International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) %K Algorithms BBO Biogeography-Based Evolutionary Meta-Heuristic Migration Optimization Partial Simplified Single %N 6 %P 20 %R 10.5121/ijscai.2013.2602 %T A Comprehensive Comparison of the Original Forms of Biogeography-Based Optimization Algorithms %U http://airccse.org/journal/ijscai/papers/2613ijscai02.pdf %V 2 %X Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of metaheuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. After three months, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications and hybridizations were employed to boost-up the performance of BBO and solve its weak exploration. However, the literature lacks the explanations and the reasons on which the modifications of the BBO forms are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through 23 benchmark functions with different dimensions and complexities. The final judgment is confirmed by evaluating the performance based on the effect of the problem’s dimensions, the side constraints and the population size. The results show that both single and simplified single migration based BBOs are faster, but have less performance as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem’s complexity and dimensions, and the values of the upper and lower side constraints. The partial migration model wins when these factors, except the population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature

@article{noauthororeditor, abstract = {Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of metaheuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. After three months, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications and hybridizations were employed to boost-up the performance of BBO and solve its weak exploration. However, the literature lacks the explanations and the reasons on which the modifications of the BBO forms are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through 23 benchmark functions with different dimensions and complexities. The final judgment is confirmed by evaluating the performance based on the effect of the problem’s dimensions, the side constraints and the population size. The results show that both single and simplified single migration based BBOs are faster, but have less performance as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem’s complexity and dimensions, and the values of the upper and lower side constraints. The partial migration model wins when these factors, except the population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in literature}, added-at = {2018-08-16T06:21:23.000+0200}, author = {Alroomi, Ali R. and Albasri, Fadhel A. and H.Talaq, Jawad}, biburl = {https://www.bibsonomy.org/bibtex/2ddb7f819f68e8c20c75c3a4079e057ab/leninsha}, doi = {10.5121/ijscai.2013.2602}, interhash = {39ac24b85dd98e6ad55b9fa05684d571}, intrahash = {ddb7f819f68e8c20c75c3a4079e057ab}, issn = {2319 - 1015}, journal = {International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI)}, keywords = {Algorithms BBO Biogeography-Based Evolutionary Meta-Heuristic Migration Optimization Partial Simplified Single}, month = {December}, number = 6, pages = 20, timestamp = {2018-08-16T06:21:23.000+0200}, title = {A Comprehensive Comparison of the Original Forms of Biogeography-Based Optimization Algorithms}, url = {http://airccse.org/journal/ijscai/papers/2613ijscai02.pdf}, volume = 2, year = 2013 }