Adaptive neighbourhood for locally and globally tuned biogeography based optimization algorithm

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.

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) 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.

This chapter presents the results of application of Jaya algorithm and its variants like SAMP-Jaya and SAMPE-Jaya algorithms on 15 unconstrained benchmark functions given in CEC 2015 as well as 15 other unconstrained functions and 5 constrained benchmark functions. The results are compared with those given by the other well known optimization algorithms. The results have shown the satisfactory performance of Jaya algorithm and its variants for the considered CEC 2015 benchmark functions and the other constrained and unconstrained optimization problems. The statistical tests have also supported the performance supremacy of the variants of the Jaya algorithm.

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.

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.

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.

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.

Task scheduling, which plays a crucial role in cloud computing and is the critical factor influencing the performance of cloud computing, is an NP-hard problem that can be solved with a heuristic algorithm. In this paper, we propose a novel heuristic algorithm, called biogeography-based optimization (BBO), and a new hybrid migrating BBO (HMBBO) algorithm, which integrates the migration strategy with particle swarm optimization (PSO). Both methods are proposed to solve the problem of scheduling-directed acyclic graph tasks in a cloud computing environment. The basic idea of our approach is to exploit the advantages of the PSO and BBO algorithms while avoiding their drawbacks. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. Both simulation and real-life experiments are conducted to verify the effectiveness of HMBBO. The experiment shows that compared with several classic heuristic algorithms, HMBBO has advantages in terms of global search ability, fast convergence rate and a high-quality solution, and it provides a new method for task scheduling in cloud computing.

Biogeography-based optimization for identifying promising compounds in chemical process

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

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.

This chapter introduces the basic concepts of biogeography based optimization (BBO) algorithm and its application to a combinatorial water switching problem. Water switching optimization is a pump scheduling problem which considers minimization of total electrical energy requirement as an objective function. Pump status (switch on/switch off) of pumping stations are considered as a discrete (binary) decision variables for the optimization problem. Suction and discharge pressure are considered as constraints in the procedure. A case study with 10 pumping station and 40 pumps is presented for the experimentation. The performance of BBO is tested against other state-of-the art algorithms that includes genetic algorithm (GA), branch & bound method (B&B), harmony search (HS) algorithm, particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms. Water pump switching problem is also investigated by using different constraint handling techniques and by considering different pump situations in a pumping station. The Computational results indicate that BBO is an appropriate algorithm to solve water pump switching problem and is effective over other optimization methods. Moreover, 20 alternative optimum solutions are presented to demonstrate water switching problem as a multi-modal problem with different optimum solutions and the search capability of BBO to find alternate optimum solutions.

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.

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