Biogeography-based Optimization Variants Research Articles

An adaptive biogeography-based optimization with cumulative covariance matrix for rule-based network intrusion detection

Biogeography-based optimization (BBO) is a well-known population-based metaheuristic optimization algorithm. The historical population distribution information is so underutilized that the global optimal value may not be searched efficiently in existing variants of BBO. To address the limitation, we propose in this paper a BBO framework based on cumulative covariance matrix (CCM) and dub it as CCM-BBO. The CCM operator is used in CCM-BBO to establish Eigen coordinate system and enables the migration operator independent on fixed coordinate system. By embedding CCM operator into BBO, the migration operator is implemented in the Eigen coordinate system to enhance the rotation invariance of BBO. To verify the performance of CCM operator, CCM-BBO has been applied to three state-of-the-art variants of BBO: SBBO, DE/BBO and EMBBO. The experimental results on 25 benchmark functions of CEC2005, 10 benchmark functions of CEC2020, and a real-world intrusion detection optimization problem have demonstrated that CCM-BBO is an effective framework to enhance the performance of BBO.

A Selective Biogeography-Based Optimizer Considering Resource Allocation for Large-Scale Global Optimization

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.

Participative Biogeography-Based Optimization

Biogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. the original BBO sometimes has not resulted in desirable outcomes. Migration, mutation and elitism are three Principal operators in BBO. The migration operator plays an important role in sharing information among candidate habitats. This paper proposes a novel migration operator in Original BBO. The proposed BBO is named as PBBO and new migration operator is examined over 12 test problems. Also, results are compared with original BBO and others Meta-heuristic algorithms. Results show that PBBO outperforms over basic BBO and other considered variants of BBO.

Improved Biogeography-Based Optimization Algorithm and Its Application to Clustering Optimization and Medical Image Segmentation

In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, an improved BBO algorithm, that is, worst opposition learning and random-scaled differential mutation BBO (WRBBO), is presented in this paper. First, BBO's mutation operator is deleted to reduce the computational complexity and a more efficient random-scaled differential mutation operator is merged into BBO's migration operator to obtain global search ability. Second, in order to balance exploration and exploitation, the BBO's migration operator is replaced with a dynamic heuristic crossover to enhance the local search ability. Finally, a worst opposition learning is merged into the improved algorithm to avoid trapping into local optima. A large number of experiments are made on 18 various kinds of classic benchmark functions and some complex functions from the CEC-2013 test set. In addition, WRBBO is applied to clustering optimization and medical image segmentation. The experimental results show that WRBBO has better optimization efficiency on benchmark function optimization, clustering optimization, and medical image segmentation than quite a few state-of-the-art BBO variants and other algorithms.

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

Biogeography-based optimization (BBO) has drawn a lot of attention as its outstanding performance. However, same with certain typical swarm optimization algorithm, BBO severely suffers from premature convergence problem and the rotational variance of migration operator. In this paper, a two-stage differential biogeography-based optimization (TDBBO) is proposed to address the premature convergence problem and alleviate the rotational variance. In the migration operator, the emigration model is selected according to the two-stage mechanism. The constant emigration model is employed to maintain the diversity of population in the early evolutionary process. The sinusoidal emigration model is selected to accelerate the convergence speed in the late evolutionary process. Meanwhile, the BBO/current-to-select/1, which is a rotationally invariant arithmetic crossover operator, is designed to alleviate the rotational variance. The standard mutation operator is replaced by the Gaussian mutation operator to jump out the local optimum effectively. The greedy selection strategy is introduced to accelerate the convergence speed after the migration and the mutation operators. Besides, the convergence performance of TDBBO is analyzed with the Markov model. Compared with the standard BBO and other outstanding BBO variants on CEC 2017 benchmarks, the TDBBO is superior to the state-of-art BBO variants in terms of solution quality, convergence speed and stability. The TDBBO lays a solid foundation for solving optimization problems of expert and intelligent systems.

Biogeography-based optimization with covariance matrix based migration

Biogeography-based optimization (BBO) is a new evolutionary algorithm. The major problem of basic BBO is that its migration operator is rotationally variant, which leaves BBO performing poorly in non-separable problems. To overcome this drawback of BBO, in this paper, we propose the covariance matrix based migration (CMM) to relieve BBO’s dependence upon the coordinate system so that BBO’s rotational invariance is enhanced. By embedding the CMM into BBO, we put forward a new BBO approach, namely biogeography-based optimization with covariance matrix based migration, called CMM-BBO. Specifically, CMM-BBO algorithms are developed by the CMM operator being randomly combined with the original migration in various existing BBO variants. Numeric simulations on 37 benchmark functions show that our CMM-BBO approach effectively improves the performance of the existing BBO algorithms.

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.

Salient object detection using biogeography-based optimization to combine features

Salient object detection aims to automatically localize a foreground object with respect to its background in an image. It plays a crucial role in a wide range of computer vision and multimedia applications. In this work, we propose an improved salient object detection method based on biogeography-based optimization, a relatively new bio-inspired metaheuristic algorithm that searches for the global optimum using a migration model. Our approach consists of two steps. In the first step, a set of local (multi-scale contrast), regional (center-surround histogram), and global (color spatial distribution) salient feature maps are extracted and normalized. In the second step, an optimal weight vector for combining these feature maps into one saliency map is determined using biogeography-based optimization and improved variants of this algorithm. As a result, a salient objects were identified and labeled as distinct from the image background. We implemented our method using three biogeography-based optimization variants, and compared our results for three popular databases against two other state-of-the-art approaches. The experimental results demonstrate that our method exhibits refined and consistent detection of salient objects.

Effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO)

Hybridization in optimization methods plays a very vital role to make it effective and efficient. Different optimization methods have different search tendency and it is always required to experiment the effect of hybridizing different search tendency of the optimization algorithm with each other. This paper presents the effect of hybridizing Biogeography-Based Optimization (BBO) technique with Artificial Immune Algorithm (AIA) and Ant Colony Optimization (ACO) in two different ways. So, four different variants of hybrid BBO, viz. two variants of hybrid BBO with AIA and two with ACO, are developed and experimented in this paper. All the considered optimization techniques have altogether a different search tendency. The proposed hybrid method is tested on many benchmark problems and real life problems. Friedman test and Holm–Sidak test are performed to have the statistical validity of the results. Results show that proposed hybridization of BBO with ACO and AIA is effective over a wide range of problems. Moreover, the proposed hybridization is also effective over other proposed hybridization of BBO and different variants of BBO available in the literature.

Enhancing the performance of biogeography-based optimization using polyphyletic migration operator and orthogonal learning

Biogeography-based optimization (BBO) is a powerful population-based algorithm inspired by biogeography and has been extensively applied to many science and engineering problems. However, its direct-copying-based migration and random mutation operators make BBO possess local exploitation ability but lack global exploration ability. To remedy the defect and enhance the performance of BBO, an enhanced BBO variant, called POLBBO, is developed in this paper. In POLBBO, a proposed efficient operator named polyphyletic migration operator can formally utilize as many as four individuals’ features to construct a new solution vector. This operator cannot only generate new features from more promising areas in the search space, but also effectively increase the population diversity. On the other hand, an orthogonal learning (OL) strategy based on orthogonal experimental design is employed. The OL strategy can quickly discover more useful information from the search experiences and efficiently utilize the information to construct a more promising solution, and thereby provide a systematic and elaborate reasoning method to guide the search directions of POLBBO. The proposed POLBBO is verified on a set of 24 benchmark functions with diverse complexities, and is compared with the basic BBO, five state-of-the-art BBO variants, five existing OL-based algorithms, and nine other evolutionary algorithms. The experimental results and comparisons demonstrate that the polyphyletic migration operator and the OL strategy can work together well and enhance the performance of BBO significantly in terms of the quality of the final solutions and the convergence rate.

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.

Multi-objective Gain-Impedance Optimization of Yagi-Uda Antenna using NSBBO and NSPSO

Biogeography-Based Optimization (BBO) is a population based algorithm which has shown impressive performance over other Evolutionary Algorithms (EAs). BBO algorithm is based on the study of distribution of biological organisms over space and time. YagiUda antenna design is most widely used antenna at VHF and UHF frequencies due to high gain, directivity and ease of construction. However, designing a Yagi-Uda antenna, that involves determination of optimal wire-lengths and their spacings, is highly complex and non-linear engineering problem. It further complicates as multiple objectives, viz. gain, and impedance, etc., are required to be optimized due to their conflicting nature, i.e., reactive antenna impedance increases significantly as antenna gain is intended to increase. In this paper Non-dominated Sorting BBO (NSBBO) is proposed and where standard and blended variants of BBO are investigated in optimizing six-element Yagi-Uda antenna designs for multiple objectives, viz., gain and impedance, where ranking of potential solutions is done using non-dominated sorting. The simulation results of BBO variants and Particle Swarm Optimization (PSO) are presented in the ending sections of the paper that depict clearly that NSBBO with blended migration operator is best option among all.

An improved biogeography based optimization approach for segmentation of human head CT-scan images employing fuzzy entropy

The present paper proposes the development of a three-level thresholding based image segmentation technique for real images obtained from CT scanning of a human head. The proposed method utilizes maximization of fuzzy entropy to determine the optimal thresholds. The optimization problem is solved by employing a very recently proposed population-based optimization technique, called biogeography based optimization (BBO) technique. In this work we have proposed some improvements over the basic BBO technique to implement nonlinear variation of immigration rate and emigration rate with number of species in a habitat. The proposed improved BBO based algorithm and the basic BBO algorithm are implemented for segmentation of fifteen real CT image slices. The results show that the proposed improved BBO variants could perform better than the basic BBO technique as well as genetic algorithm (GA) and particle swarm optimization (PSO) based segmentation of the same images using the principle of maximization of fuzzy entropy.