Biogeography-Based Optimization

Evolutionary Computation, 2011

Biogeography-based optimization (BBO) is a population-based evolutionary algorithm (EA) that is based on the mathematics of biogeography. Biogeography is the study of the geographical distribution of biological organisms. We present a simplified version of BBO and perform an approximate analysis of the BBO population using probability theory. Our analysis provides approximate values for the expected number of generations before the population's best solution improves, and the expected amount of improvement. These expected values are functions of the population size. We quantify three behaviors as the population size increases: first, we see that the best solution in the initial randomly generated population improves; second, we see that the expected number of generations before improvement increases; and third, we see that the expected amount of improvement decreases.

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 were employed to enhance the performance of BBO. However, the literature lacks the explanations and the reasons on which the modifications are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through a variety of benchmark functions with different dimensions and complexities. 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 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.

Information Sciences, 2013

Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and real-world optimization problems. Motivated by biogeography theory and previous results, three variations of BBO migration are introduced in this paper. We refer to the original BBO algorithm as partial immigration-based BBO. The new BBO variations that we propose are called total immigration-based BBO, partial emigration-based BBO, and total emigrationbased BBO. Their corresponding Markov chain models are also derived based on a previously-derived BBO Markov model. The optimization performance of these BBO variations is analyzed, and new theoretical results that are confirmed with simulation results are obtained. Theoretical results show that total emigration-based BBO and partial emigration-based BBO perform the best for three-bit unimodal problems, partial immigrationbased BBO performs the best for three-bit deceptive problems, and all these BBO variations have similar results for three-bit multimodal problems. Performance comparison is further investigated on benchmark functions with a wide range of dimensions and complexities. Benchmark results show that emigration-based BBO performs the best for unimodal problems, and immigration-based BBO performs the best for multimodal problems. In addition, BBO is compared with a stud genetic algorithm (SGA), standard particle swarm optimization (SPSO 07), and adaptive differential evolution (ADE) on real-world optimization problems. The numerical results demonstrate that BBO outperforms SGA and SPSO 07, and performs similarly to ADE for the real-world problems.

Computers & Operations Research, 2012

Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm and it has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. The present paper shows how BBO can be applied for constrained optimization problems, where the objective is to find a solution for a given objective function, subject to both inequality and equality constraints. To solve such problems, the present work proposes three new variations of BBO. Each new version uses different update strategies, and each is tested on several benchmark functions. A successful implementation of an additional selection procedure is also proposed in this work which is based on the feasibility-based rule to preserve fitter individuals for subsequent generations. Our extensive experimentations successfully demonstrate the usefulness of all these modifications proposed for the BBO algorithm that can be suitably applied for solving different types of constrained optimization problems.

Neural Computing and Applications, 2018

Classification is a data mining task that assigns items in a collection to predefined categories or classes, also referred to as supervised learning. The goal of classification is to accurately predict the target class for each case in the data. A review of the literature shows that many algorithms, including statistical and machine learning algorithms, have been successfully used to handle classification problems in different areas, but their performance varies considerably. Even though the neural network is effective in addressing a wide range of problems, to date no specific neural network approach has been found that can ensure that the optimal solution is arrived at when solving classification problems. Some of the important challenges include finding the most appropriate weight parameter for the classifier through the implementation of population-based approaches; attaining a balance between the processes of exploration and exploitation by employing hybridization methods; and obtaining fast convergence by controlling random movement and by generating good initial solutions. This study investigates how can good initial populations drive higher convergence speed and better classification accuracy in solving classification problems. Local search (in this case, the simulated annealing algorithm) is used to produce an initial solution for the classification problem and then a heuristic initialization hybridized with biogeographybased optimization is applied. The proposed approaches are tested on 11 standard benchmark datasets. This is a new approach in the classification arena, and it represents an approach that outperforms the current state of the art on most of the tested benchmark datasets.

Information Sciences, 2014

Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses migration to share information among candidate solutions. One limitation of BBO is that it changes only one independent variable at a time in each candidate solution. In this paper, a linearized version of BBO, called LBBO, is proposed to reduce rotational variance. The proposed method is combined with periodic re-initialization and local search operators to obtain an algorithm for global optimization in a continuous search space. Experiments have been conducted on 45 benchmarks from the 2005 and 2011 Congress on Evolutionary Computation, and LBBO performance is compared with the results published in those conferences. The results show that LBBO provides competitive performance with state-of-the-art evolutionary algorithms. In particular, LBBO performs particularly well for certain types of multimodal problems, including high-dimensional real-world problems; LBBO is insensitive to whether or not the solution lies on the search domain boundary; LBBO is insensitive to whether or not the global optimum lies in a wide or narrow basin; and LBBO is insensitive to whether or not the global optimum lies within or outside the initialization domain.

2013

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 and hybridizations were employed to enhance its performance. The researchers found that the original version of BBO has some weakness on its exploration. This paper tries to solve the root problems itself instead of solving its effect by using different techniques. It 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, which is located after the mutation stage, is sized to avoid any corruption on the suitability index variables of the non-mutated islands. The proposed modifications are extensively tested on 120 test functions with differen...

IEEE Transactions on Emerging Topics in Computational Intelligence, 2017

Reply: In the revision, we have added the latest references, including these mentioned by the reviewer. Reviewer # 3 8. The review is extensive and in depth. However, in part VI (MATHEMATICAL THEORIES OF BBO), comparison of different BBO models was expected with their advantages and disadvantages. It would have given in depth knowledge of the evolution of BBO techniques. Reply: Thanks for this important comment. In the revision, we have added the advantages and disadvantages of different BBO models in the end of the second paragraph and in the middle of the third paragraph in Section VI.

Engineering Applications of Artificial Intelligence, 2011

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BRO outperforms standard BBO. Second, we employ blended BRO to solve constrained optimization problems. Constraints are handled by modifying the BRO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BRO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained singleobjective optimization problems.

Journal of King Saud University - Computer and Information Sciences, 2018

Biogeography Based Optimization (BBO) is a population based metaheuristic algorithm using the idea of migration and mutation operation of species for solving complex optimization problems. BBO has demonstrated good performance on various unconstrained and constrained benchmark functions. It has also been applied to real world optimization problems of type linear or nonlinear, nominal or ordinal as well as mixed variables. But, it is realized that adaptation of the intensification and diversification for solving complex optimization problems are challenging tasks. To cope with these challenges, we develop a novel migration model for BBO which inherits features of the nearest neighbour of the local best individual to be migrated along with a global best individual of the pool. Furthermore to select the local best individual for the habitat to be migrated an adaptive local topological structure has been used. We name it as ''Adaptive Neighbourhood for Locally and Globally Tuned Biogeography Based Optimization algorithm (ANLGBBO)". This maintains the balance between intensification and diversification i.e., improve solution by exploiting the accumulated search space and exploring the large space by identifying regions with high quality solutions. We have carried out an extensive numerical evaluation and comparisons for experimental tests using twenty benchmark functions with different features to measure the efficiency of the algorithm. The experimental study confirms ANLGBBO draws clear line of other variants of BBO algorithms in terms of population diversity and establish the accuracy of global optimal solution.