Science Of Biogeography Research Articles

Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules

BBO is one of the new metaheuristic optimization algorithms, which is based on the science of biogeography. It can be used to solve optimization problems through the migration and drift of species between habitats. Many improved BBO algorithms have been proposed, but there were still many shortcomings in global optimization, convergence speed, and algorithm complexity. In response to the above problems, this paper proposes an improved BBO algorithm (DCGBBO) by hierarchical tissue-like P system with triggering ablation rules. Membrane computing is a branch of natural computing that aims to abstract computational models (P system) from the structure and function of biological cells and from the collaboration of cell groups such as organs and tissues. In this paper, firstly, a dynamic crossover migration operator is generated to improve the global search ability and also increase the species diversity. Secondly, a dynamic Gaussian mutation operator is introduced to speed up convergence and improve local search capabilities. To guarantee the correctness and feasibility of the mutation, a unified maximum mutation rate is designed. Finally, a hierarchical tissue-like P system with triggering ablation rules is combined with the DCGBBO algorithm, making use of evolution rules and communication rules to achieve migration and mutation of solutions and reduce computational complexity. In the experiments, eight classic benchmark functions and CEC 2017 benchmark functions are applied to demonstrate the effect of our algorithm. We apply the proposed algorithm to segment four colour pictures, and the results proved to be better compared to other algorithms.

NBBO: A new variant of biogeography-based optimization with a novel framework and a two-phase migration operator

Biogeography-based optimization (BBO) is a novel evolutionary algorithm, which is proposed with inspiration from the science of biogeography, to solve global optimization problems. To overcome the inability of BBO to make a good balance between its exploration and exploitation abilities, this paper introduces a new variant of BBO, which is called NBBO. The framework of NBBO considers two or more sub-iterations in an iteration of the algorithm to perform the evolution process. In each sub-iteration, a sample (sub-population) is selected from the input population of the iteration, based on a triangular probability distribution, to choose emigrating habitats (solutions) from. On the other hand, a novel two-phase migration operator is used in the framework of NBBO to make the algorithm effectively explore a search space. By making a good balance between its exploration and exploitation abilities, which is conducted by its new framework, NBBO can escape from local optima. Quantitative evaluations, based on extensive experiments on a set of 23 benchmark functions with diverse complexities, reveal that NBBO achieves favorable results which are quite superior to the results of other relevant state-of-the-art swarm intelligence-based and evolutionary algorithms.

Enhanced Biogeography-based Optimization: A New Method for Size and Shape Optimization of Truss Structures with Natural Frequency Constraints

The current study presents an enhanced biogeography-based optimization (EBBO) algorithm for size and shape optimization of truss structures with natural frequency constraints. The BBO algorithm is one of the recently developed meta-heuristic algorithms inspired by the mathematical models in biogeography science and is based on the migration behavior of species among the habitats in the nature. In this study, the overall performance of the standard BBO algorithm is enhanced by new migration and mutation operators. The efficiency of the proposed algorithm is demonstrated by utilizing four benchmark truss design examples with frequency constraints. Numerical results show that the proposed EBBO algorithm not only significantly improves the performance of the standard BBO algorithm, but also finds competitive results compared with recently developed optimization methods.

A survey of biogeography-based optimization

Optimization is a classical issue and in many areas that are bound up with people’s daily life. In current decades, with the development of human civilization and industry society, many complicated optimization problems are raised. In the meantime, corresponding novel approaches are constantly proposed for solving these problems. One of them is meta-heuristics, which is inspired from natural phenomena and contains many kinds of algorithms. The classical meta-heuristic algorithms have exhibited their superiority in dealing optimization problems, especially for specific problems such as combinatorial optimization. As a novel meta-heuristic algorithm, biogeography-based optimization (BBO), inspired from the science of biogeography, has its own characteristics and exhibits a huge potential in computation and optimization. According to current investigations and analysis on this algorithm, it has not only achieved a great success in numerical optimization problems, but also been implemented in various kinds of applications, and drawn worldwide attentions. In this paper, we present a survey for this algorithm. First, we introduce the basic operators of BBO, including migration and mutation. For migration operator, it mimics species migration among islands, which provides a recombination way for candidate solutions to interact with each other so that the whole population can be improved. Besides linear migration model, several other popular migration models are also introduced and the corresponding performances are analyzed. For mutation operator, the design of BBO is different from other meta-heuristics. In standard BBO, different candidate solutions have different migration rates and the rate assignment is influential to BBO’s performance. Second, we summarized some popular variants of BBO and related hybrid algorithms that significantly enhance BBO’s performance. This part introduces the development of this algorithm and helps readers understand the way to choose a suitable version of BBO for a given problem. The way to improve algorithms’ performances helps readers design new variants of BBO for specific problems. Third, we present the evaluation of BBO’s performance for both numerical and practical problems. The results demonstrate BBO is competent to solve optimization problems. Despite so many achievements of BBO, some open issues that should be considered and solved in future work in order to make this algorithm more competitive in meta-heuristics.

Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models

Array thinning is a common discrete-valued combinatorial optimization problem. Evolutionary algorithms are suitable techniques for above-mentioned problem. Biogeography-Based Optimization (BBO), which is inspired by the science of biogeography, is a stochastic population-based evolutionary algorithm (EA). The original BBO uses a linear migration model to describe how species migrate from one island to another. Other nonlinear migration models have been proposed in the literature. In this paper, we apply BBO with four different migration models to five different large array design cases. Additionally we compare results with five other popular algorithms. The problems dimensions range from 150 to 300. The results show that BBO with sinusoidal migration model generally performs better than the other algorithms. However, these results are considered to be indicative and do not generally apply to all optimization problems in antenna design.

Backtracking biogeography-based optimization for numerical optimization and mechanical design problems

As a novel Evolutionary Algorithm (EA), Biogeography-Based Optimization (BBO), inspired by the science of biogeography, draws much attention due to its significant performance in both numerical simulations and practical applications. In BBO, the features in poor solutions have a large probability to be replaced by the features in good solutions. The replacement operator is termed migration. However, the replacement causes a loss of the features in poor solutions, breaks the diversity of population and may lead to a local optimal solution. To overcome this, we design a novel migration operator to propose Backtracking BBO (BBBO). In BBBO, besides the regular population, an external population is employed to record historical individuals. The size of external population is the same as the size of regular population. The external population and regular population are used together to generate the next population. After that, the individuals in external population are randomly selected to be updated by the individuals in current population. In this way, the external population in BBBO can be considered as a memory to take part in the evolutionary process. The memory takes into account both current and historical data to generate next population, which enhances algorithm's ability in exploring searching space. In numerical simulation, 14 classical benchmarks are employed to test BBBO's performance and several classical nature inspired algorithms are use in comparison. The results show that the strategy in BBBO is feasible and very effective to enhance algorithm's performance. In addition, we apply BBBO to mechanical design problems which involve constraints in optimization. The comparison results also exhibit that BBBO is very competitive in solving practical optimization problems.

A biogeography-based optimization for optimum discrete design of skeletal structures

This article presents a modified biogeography-based optimization (MBBO) algorithm for optimum design of skeletal structures with discrete variables. The main idea of the biogeography-based optimization (BBO) algorithm is based on the science of biogeography, in which each habitat is a possible solution for the optimization problem in the search space. This algorithm consists of two main operators: migration and mutation. The migration operator helps the habitats to exploit the search space, while the mutation operator guides habitats to escape from the local optimum. To enhance the performance of the standard algorithm, some modifications are made and an MBBO algorithm is presented. The performance of the MBBO algorithm is evaluated by optimizing five benchmark design examples, and the obtained results are compared with other methods in the literature. The numerical results demonstrate that the MBBO algorithm is able to show very competitive results and has merits in finding optimum designs.

Biogeography-based optimization for identifying promising compounds in chemical process

Identifying promising compounds from a vast collection of potential compounds is an important and yet challenging problem in chemical engineering. An efficient solution to this problem will help to reduce the expenditure at the early states of chemical process. In an attempt to solve this problem, the industry is looking for predictive tools that would be useful in testing optimal properties of a candidate compound earlier. This paper explores the application of biogeography-based optimization (BBO) to achieve such predictive work. BBO is a new evolutionary algorithm that is based on the science of biogeography. BBO is a population-based search method that achieves information sharing by species migration. The performance of BBO is compared with genetic algorithm (GA) and particle swarm optimization (PSO) on a set of test functions and the cases of identifying promising compounds. Simulation results show that BBO is a competitive method in determining an optimal solution to the optimization of promising compounds.

Introduction of Biogeography-Based Programming as a new algorithm for solving problems

Application of evolutionary computation techniques is relatively novel for machine learning. Motivated by different types of evolutionary computation techniques, different types of automatic programming were proposed. Biogeography-Based Optimization (BBO) is a new evolutionary algorithm that is inspired by the science of biogeography and has been shown to be competitive to other population-based algorithms. Inspired by biogeography theory and previous results, in this paper Biogeography-Based Programming (BBP) is proposed as a new type of automatic programming for creating polynomial regression models. In order to show the effectiveness of the proposed BBP, a number of experiments were carried out on a suite set of benchmark functions and the results were also compared with several existing automatic programming algorithms. Furthermore, sensitivity analysis was performed for the parameter settings of the proposed BBP. The results indicate that the proposed model is promising in terms of success rate and accuracy and it performs better than other algorithms investigated in this consideration.

A new method for multi-objective in core fuel management optimization using biogeography based algorithm

In this investigation, we developed a new optimization method, i.e., biogeography based optimization (BBO), for loading pattern optimization problem of pressurized water reactors. BBO is a novel stochastic force based on the science of biogeography. Biogeography is the schoolwork of geographical allotment of biological organisms. BBO make use of migration operator to share information between the problem solutions. The problem solutions are called as habitats and sharing of features is called migration. For the evaluation of the proposed method, we applied a multi-objective fitness function i.e., the maximization of reactivity at BOC and the flattening of power distribution are achieved efficiently and simultaneously. The neutronic calculation is done by CITATION and WIMS codes.

Biogeography-based optimization in noisy environments

Biogeography-based optimization (BBO) is a new evolutionary optimization algorithm that is based on the science of biogeography. In this paper, BBO is applied to the optimization of problems in which the fitness function is corrupted by random noise. Noise interferes with the BBO immigration rate and emigration rate, and adversely affects optimization performance. We analyse the effect of noise on BBO using a Markov model. We also incorporate re-sampling in BBO, which samples the fitness of each candidate solution several times and calculates the average to alleviate the effects of noise. BBO performance on noisy benchmark functions is compared with particle swarm optimization (PSO), differential evolution (DE), self-adaptive DE (SaDE) and PSO with constriction (CPSO). The results show that SaDE performs best and BBO performs second best. In addition, BBO with re-sampling is compared with Kalman filter-based BBO (KBBO). The results show that BBO with re-sampling achieves almost the same performance as KBBO but consumes less computational time.

A hybrid biogeography-based optimization algorithm for job shop scheduling problem

In this paper, a hybrid biogeography-based optimization (HBBO) algorithm has been proposed for the job-shop scheduling problem (JSP). Biogeography-based optimization (BBO) is a new bio-inpired computation method that is based on the science of biogeography. The BBO algorithm searches for the global optimum mainly through two main steps: migration and mutation. As JSP is one of the most difficult combinational optimization problems, the original BBO algorithm cannot handle it very well, especially for instances with larger size. The proposed HBBO algorithm combines the chaos theory and “searching around the optimum” strategy with the basic BBO, which makes it converge to global optimum solution faster and more stably. Series of comparative experiments with particle swarm optimization (PSO), basic BBO, the CPLEX and 14 other competitive algorithms are conducted, and the results show that our proposed HBBO algorithm outperforms the other state-of-the-art algorithms, such as genetic algorithm (GA), simulated annealing (SA), the PSO and the basic BBO.

An analysis of the migration rates for biogeography-based optimization

Biogeography-Based Optimization (BBO), inspired by the science of biogeography, is a novel population-based Evolutionary Algorithm (EA). For optimization problems, BBO builds the matching mathematical model of the organism distribution. In this evolutionary mechanism, species migrating among islands can be considered as the information transition among different solutions represented by habitats. Solutions are reassembled according to migration rates. However, so far, the migration models are generally designed by empirical studies. This leads to immature conclusions that are unreliable. To complete the previous works, this paper investigates transition probability matrices of BBO to clarify that the transition probability of median number of species is not the only determinant factor to influence performance. The impact of migration rates on BBO is mathematically discussed, which is helpful to design migration models. Using numerical simulations, the BBO and several other classical evolutionary algorithms are compared. The simulations also comprehensively explain the effect of the BBO’s properties on its performance including dimension, population size, and migration models. The results validate the theoretical analysis in this paper.

Cauchy Biogeography-Based Optimization based on lateral inhibition for image matching

In this paper, a hybrid method of Cauchy Biogeography-Based Optimization (CBBO) and Lateral Inhibition (LI) is proposed to complete the task of complicated image matching. Lateral inhibition mechanism is adopted for image pre-process to make the intensity gradient in the image contrastively strengthened. Biogeography-Based Optimization (BBO) is a bio-inspired algorithm for global optimization which is based on the science of biogeography, searching for the global optimum mainly through two steps: migration and mutation. To promote the optimization performance, an improved version of the BBO method using Cauchy mutation operator is proposed. Cauchy mutation operator enhances the exploration ability of the algorithm and improves the diversity of population. The proposed LI-CBBO method for image matching inherits both the advantages of CBBO and lateral inhibition mechanism. Series of comparative experiments using Particle Swarm Optimization (PSO), LI-PSO, BBO and LI-BBO have been conducted to demonstrate the feasibility and effectiveness of the proposed LI-CBBO.

Multi-strategy ensemble biogeography-based optimization for economic dispatch problems

Economic dispatch (ED) is an important task in power system operation. It is able to decrease the operating cost, save energy resources, and reduce environmental load. In this paper, a multi-strategy ensemble biogeography-based optimization (MsEBBO) based method for ED problems is proposed. BBO is a population-based meta-heuristic algorithm inspired by the science of biogeography and mainly consists of three components: migration model, migration operator, and mutation operator. It has good local exploitation ability but lacks satisfactory global exploration ability. To keep a proper balance between exploration and exploitation, MsEBBO has three extensions to BBO’s three components according to the no free lunch theorem. First, a nonlinear migration model based on sinusoidal curve is employed. Second, a backup migration operator through adopting a backup strategy to combine perturb operator and blended operator is presented. This operator can make the entire population fully exchange or share information and thus further strengthen the exploitation ability. Finally, both differential mutation and Lévy local search are embedded as mutation operator for MsEBBO using a similar backup strategy. Gaining from this mutation operator, MsEBBO can be accelerated to escape from local optima and perform efficient search within global range. Additionally, an effective repair technique is proposed to handle different constraints of ED problems. The performance of MsEBBO is tested on four ED problems with diverse complexities. Experimental results and comparisons with other recently reported ED solution methods confirm that MsEBBO is capable of yielding a good balance between exploration and exploitation, and obtaining competitive solution quality. Moreover, the sensitivity of MsEBBO to variations in population size is investigated as well.

Migration Effects on BBO Evolution in Optimizing Fifteen Element Yagi-Uda Antenna Design

Biogeography Based Optimization (BBO) is a recently introduced optimization technique based on science of biogeography, i.e., study of distribution of biological species over space and time. In BBO, potential solutions of a problem are grouped in integer vectors known as habitats. Feature, i.e., Suitability Index Variable (SIV), sharing among various habitats is made to occur with migration operator where as exploration of new SIVs is done with mutation operator. Different migration variants are proposed to increase the diversity in the population, with objective of improved performance of BBO algorithm. Yagi-Uda antenna is a widely used antenna design due to various useful properties of high gain, low cost and ease of construction. Designing a Yagi-Uda antenna involves determination of element lengths and spacings between them to get desired radiation characteristics. In this paper, various migration variants of BBO algorithm, reported till date, are investigated to optimize the lengths and spacings for Yagi-Uda antenna elements for maximum gain. The results obtained with these migration variants are compared and the best results are presented in the ending sections of the paper.

Biogeography-based optimisation of Cognitive Radio system

Biogeography-based optimisation (BBO) is a novel population-based global optimisation algorithm that is stimulated by the science of biogeography. The mathematical models of biogeography describe how a species arises, migrates from one habitat (Island) to another or gets extinct. BBO searches for the global optimum mainly through two steps: migration and mutation. These steps are controlled by immigration and emigration rates of the species in the habitat which are also used to share information between the habitats. In this paper, BBO has been applied to Cognitive Radio (CR) system for optimising its various transmission parameters to meet the quality of service (QoS) that is defined by the user in terms of minimum transmit power, minimum bit error rate (BER), maximum throughput, minimum interference and maximum spectral efficiency. To confirm the capability of biogeography-based optimisation algorithm, the results obtained by BBO are compared with that obtained by using genetic algorithm (GA) for the various QoS parameters, and it has been observed that BBO outperforms GA in system optimisation.

Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants

In a Wireless Sensor Network (WSN) accurate location of target node is highly desirable as it has strong impact on overall performance of the WSN. This paper proposes the application of different migration variants of Biogeography-Based Optimization (BBO) algorithm and Particle Swarm Optimization (PSO) for distributed optimal localization of randomly deployed sensors for different ranges. Biogeography is collective learning of geographical allotment of biological organisms. BBO has a new inclusive vigor based on the science of biogeography and employs migration operator to share information between different habitats, i.e., problem solution. PSO models have only fast convergence but less mature. An investigation on distributed iterative localization is presented in this paper that shows how time consumption and error varies for different ranges. Here the nodes that get localized in iteration act as anchor node. A comparison of the performance of PSO and different migration variants of BBO in terms of number of nodes localized, localization accuracy and computation time is presented.

Synthesis of thinned planar concentric circular antenna arrays using biogeography-based optimisation

This study presents a novel optimisation algorithm biogeography-based optimisation (BBO) for thinning large multiple concentric circular ring arrays. The objective is to achieve an array of uniformly excited isotropic antennas that will generate a narrow beam with minimum relative sidelobe level (SLL). BBO is a new comprehensive force based on the science of biogeography. Biogeography is the schoolwork of geographical allotment of biological organisms. BBO utilises migration operator to share information between the problem solutions. The problem solutions are known as habitats and sharing of features is called migration. In this study, the authors propose pattern synthesis method to reduce the SLLs with narrow beamwidth (BW) by making the ring array thinned using the BBO algorithm. The thinning percentage of the array is kept equal to or more than 50% and the BW is kept equal to or less than that of a fully populated, uniformly excited and 0.5λw, spaced concentric circular ring array of same number of elements and rings. The results obtained are compared with previous published results of modified particle swarm optimisation and differential evolution with global and local neighbourhoods.

PBBO: A New Approach for Underground Water Analysis

From last ten to fifteen years many optimization techniques have been evolved and enhanced. Out of those, PSO and BBO are the two techniques that have been widely used in Swarm Optimization. PSO is better in comparison many genetic algorithms. PSO has applications in various areas like Optimization, Neural Networks training, Fuzzy controls and etc. BBO is based on science of biogeography. BBO has some features common to PSO and Genetic algorithms but it has some important features that make it more reliable than others. In this paper, we have proposed new algorithm that combines the features of PSO and BBO. It will help in providing more reliability in optimization world.