Markov Models for Biogeography-Based Optimization

A two-stage differential biogeography-based optimization algorithm and its performance analysis

An analysis of the migration rates for biogeography-based optimization

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.

As an essential factor of evolutionary algorithms (EAs), mutation operator plays an important role in exploring the search space, maintaining the diversity of individuals and breaking away local optimums. In most standard evolutionary algorithms, the mutation operator is independent from the recombination operator. Nevertheless, in biogeography-based optimization (BBO), the mutation operator is affected not only by predefined constants but also by recombination models, namely the migration operator. However to date, the relationship between the mutation and migration has never been investigated. To reveal the relationship and evaluate the mutation models, we utilize drift analysis to investigate the expected first hitting time of BBO with different migration models. The analysis compares three different kinds of mutation models in a mathematical way and the conclusion is helpful for designing migration models of BBO. The simulation results are also in agreement with our analysis.

Biogeography-based optimisation (BBO) is a relatively new bio-inspired and population-based optimisation algorithm. It is proved itself a worthy optimisation technique to solve various nonlinear optimisation problem. In this paper, the concept of predator-prey optimisation is incorporated in BBO to increase the diversity of the population. The proposed biogeography-based predator-prey optimisation (BPPO) technique is implemented on both single-input and dual-input power system stabilisers (PSSs). The effectiveness of the proposed algorithm is demonstrated on single machine infinite bus (SMIB) system using four different types of single and dual input PSS. From the simulation study, it is revealed that the transient performance of dual-input PSS is better than single-input PSS. The performance of the proposed algorithm is evaluated over a wide range of operating condition and the simulation results of the proposed method are compared with those obtained by BBO, seeker optimisation algorithm (SOA) and bacteria foraging optimisation (BFO). The results confirm the potential and effectiveness of the proposed BPPO algorithm over SOA, BFO and BBO.

Biogeography-based optimization (BBO), a recent proposed metaheuristic algorithm, has been successfully applied to many optimization problems due to its simplicity and efficiency. However, BBO is sensitive to the curse of dimensionality; its performance degrades rapidly as the dimensionality of the search space increases. In this paper, a selective migration operator is proposed to scale up the performance of BBO and we name it selective BBO (SBBO). The differential migration operator is selected heuristically to explore the global area as far as possible whist the normal distributed migration operator is chosen to exploit the local area. By the means of heuristic selection, an appropriate migration operator can be used to search the global optimum efficiently. Moreover, the strategy of cooperative coevolution (CC) is adopted to solve large-scale global optimization problems (LSOPs). To deal with subgroup imbalance contribution to the whole solution in the context of CC, a more efficient computing resource allocation is proposed. Extensive experiments are conducted on the CEC 2010 benchmark suite for large-scale global optimization, and the results show the effectiveness and efficiency of SBBO compared with BBO variants and other representative algorithms for LSOPs. Also, the results confirm that the proposed computing resource allocation is vital to the large-scale optimization within the limited computation budget.

For sharing the information between solutions, biogeography based optimization (BBO) procedure uses migration operator based on biogeography. Differential evolution (DE) is another well-organized, robust and most common evolutionary procedure for solving global optimization problems. We have implemented BBO with DE algorithm and proposes a new efficient approach namely BBO/DE. To generate an effective candidate solution BBO/DE hybridize the BBO with the DE effectively and the BBO migration operators are adjusted on the basis of iteration count to enhance the performance. To certify the effectiveness of our suggested approach, we have used 6 benchmark functions and applied our proposed solution. From the comparison results it can be induced that the suggested methodology is effective and efficient both in comparison to traditional BBO and DE.

A new Biogeography-based Optimization (BBO) algorithm for detecting community in complex network science has been proposed. It adopts integral matrix encoding that each element represents the community index of the corresponding node. The migration operator and the mutation operator are the fundamental process to improve the accuracy and the quality of community detection. Each individual in the habitat can exchange information with other individuals by immigrating or emigrating. And the exchange among individuals will change the integral matrix value that closely connects with community index. In this paper, network modularity function is chosen as its suitability function, which to measure the quality of the community structure. The quality and effectiveness of the BBO algorithm are exposed in experimental tests by using artificial random network and real networks. The accuracy of BBO algorithm is batter to that of some classic algorithms, and is comparable to that of some latest algorithms.

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.

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.

Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats. Almost 10 years have passed since the first BBO paper was published in 2008. BBO has successfully solved optimization problems in many different domains and has reached a relatively mature state. Considering the significant and expanding research on BBO and its applications, we find that the time is right to provide a 10-year anniversary review of the published literature, and also to point out some important avenues of future research. The purpose of this paper is to summarize and organize the literature related to the past 10 years of BBO research. Beginning with a foundation of basic BBO, we review the family of BBO algorithms and discuss BBO modifications, hybridizations, applications in science and engineering, and mathematical theory. Finally, the paper presents some interesting open problems and future research directions for BBO.

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.

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.

Improved biogeography-based optimization with random ring topology and Powell's method

Numerical comparisons of migration models for Multi-objective Biogeography-Based Optimization