Biogeography-Based Optimization: A 10-Year Review

Technology has enabled us to carry the world on our tips. Cloud computing has majorly contributed to this by providing infrastructure services on the go using pay per use model and with high quality of services. Cloud services provide resources through various distributed datacenters and client requests been fulfilled over these datacenters which act as resources. Therefore, resource allocation plays an important role in providing a high quality of service like utilization, network delay and finish time. Biogeography-based optimization (BBO) is an optimization algorithm that is an evolutionary algorithm used to find optimized solution. In this work BBO algorithm is been used for resource optimization problem in cloud environment at infrastructure as a service level. In past several task scheduling algorithms are being proposed to find a global best schedule to achieve least execution time and high performance like genetic algorithm, ACO and many more but as compared to GA, BBO has high probability to find global best solution. Existing solutions aim toward improving performance in term of power execution time, but they have not considered network performance and utilization of the systems performance parameters. Therefore, to improve the performance of cloud in network-aware environment we have proposed an efficient nature inspired BBO algorithm. Further, the proposed approach takes network overhead and utilization of the system into consideration to provide improved performance as compared to ACO, Genetic algorithm as well as with PSO.

Biogeography-based optimization for identifying promising compounds in chemical process

Biogeography-based optimization (BBO) is an evolutionary algorithm that is based on the science of biogeography. Biogeography is the study of the geographical distribution of organisms. In BBO, problem solutions are represented as islands, and the sharing of features between solutions is represented as migration between islands. This paper develops a Markov analysis of BBO, including the option of elitism. Our analysis gives the probability of BBO convergence to each possible population distribution for a given problem. We compare our BBO Markov analysis with a similar genetic algorithm (GA) Markov analysis. Analytical comparisons on three simple problems show that with high mutation rates the performance of GAs and BBO is similar, but with low mutation rates BBO outperforms GAs. Our analysis also shows that elitism is not necessary for all problems, but for some problems it can significantly improve performance.

Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper‐based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.

VariableZ0, a new concept in antenna design and optimization, is applied to two optimization problems: a multi-stub matching network (MSMN) using biogeography-based optimization (BBO), and an ultra wideband meander monopole antenna (MMA) using central force optimization (CFO). BBO is a newly-proposed stochastic global search and optimization evolutionary algorithm (EA) used to determine MSMN stub lengths and locations for optimum (minimum) reflection coefficient. CFO is a deterministic EA used to optimize the MMA's impedance bandwidth (IBW) while maintaining good average gain without considering the radiation pattern in detail. Two cases are investigated for both problems: (a) fixed characteristic impedanceZ0, and (b) variable characteristic impedance. In the first case,Z0is a fixed user-specified parameter (the traditional methodology), whereas in the second, it is a true variable quantity whose value is determined by the optimization methodology, which is a new technology. VariableZ0is a fundamentally different design approach in optimization problems. BBO's fixedZ0results for MSMN are compared to published data computed using Nelder–Mead optimization with BBO exhibiting better performance. BBO's results are improved even more using VariableZ0technology. A similar performance improvement is seen for VariableZ0applied to the CFO-optimized MMA.

Biogeography-based Optimization (BBO) Algorithm for Single Machine Total Weighted Tardiness Problem (SMTWTP)

This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.

In this paper some results on the analysis of a recently introduced family of optimization algorithms, named M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -BBO are presented. The algorithms have been generated starting from the Biogeography Based Optimization (BBO) algorithm, introducing new features aimed to improve the convergence and the reliability of the methods. Here, M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> -BBO is applied to the optimization of antenna problems: the results show the efficiency of the proposed schemes, that in all the considered cases outperform standard BBO.

An analysis of the migration rates for biogeography-based optimization

Adaptive neighbourhood for locally and globally tuned biogeography based optimization algorithm

A robust approach for optimal design of plate fin heat exchangers using biogeography based optimization (BBO) algorithm

Fuzzy C-means (FCM) clustering method has been widely used in image segmentation that plays an important role in a variety of applications in image processing and computer vision systems, but the performance of FCM heavily relies on the initial cluster centers which are difficult to determine. To solve the problem, the paper proposes a new hybrid method for image segmentation, which first randomly generates a population of initial clustering solutions and then uses an evolutionary algorithm to search for better clustering solutions; at each iteration, the FCM method is applied on each initial clustering solution to produce its segmentation result. Among a set of popular evolutionary algorithms, we find that the biogeography-based optimization (BBO) metaheuristic exhibits good performance on the considered problem. Besides the basic BBO, we have also proposed a set of improved BBO versions in their combination with FCM for image segmentation. Computational experiments on a set of test images show that the proposed method has significant advantage over the basic FCM algorithm and those hybrid algorithms combining FCM with other evolutionary algorithms such as artificial bee colony and particle swarm optimization.

Two defects of biogeography‐based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct‐copying migration strategy, information in several good‐quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning‐based algorithms, and other non‐OL‐based evolutionary algorithms. The effects of each improved component are also analyzed.

The biogeography-based optimisation (BBO) algorithm is a novel evolutionary algorithm inspired by biogeography. Similarly, to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real problems. Due to the novelty of this algorithm, however, there is little in the literature regarding alleviating these two problems. Chaotic maps are one of the best methods to improve the performance of evolutionary algorithms in terms of both local optima avoidance and convergence speed. In this study, we utilise ten chaotic maps to enhance the performance of the BBO algorithm. The chaotic maps are employed to define selection, emigration, and mutation probabilities. The proposed chaotic BBO algorithms are benchmarked on ten test functions. The results demonstrate that the chaotic maps (especially Gauss/mouse map) are able to significantly boost the performance of BBO. In addition, the results show that the combination of chaotic selection and emigration operators results in the highest performance.

Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from statistical mechanics. Rather than trying to exactly predict the evolution of the population, statistical mechanics methods describe the evolution of statistical properties of the population fitness. This paper uses the one-max problem, which has only one optimum and whose fitness function is the number of 1s in a binary string, to derive equations that predict the statistical properties of BBO each generation in terms of those of the previous generation. These equations reveal the effect of migration and mutation on the population fitness dynamics of BBO. The results obtained in this paper are similar to those for the simple genetic algorithm with selection and mutation. The paper also derives equations for the population fitness dynamics of general separable functions, and we find that the results obtained for separable functions are the same as those for the one-max problem. The statistical mechanics theory of BBO is shown to be in good agreement with simulation.