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.     

Blended biogeography-based optimization for constrained optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems.     

基于人工萤火虫局部决策域的改进生物地理学优化算法

针对生物地理学优化（BBO）算法搜索能力不足的缺点,提出基于萤火虫算法局部决策域策略的改进迁移操作来提算法的全局寻优能力。改进的迁移操作能够在考虑不同栖息地各自的迁入率与迁出率的基础上,进一步利用栖息地之间的相互影响关系。将改进算法应用于12个典型的函数优化问题来测试改进生物地理学优化算法的性能,验证了改进算法的有效性。与BBO、改进BBO（IBBO）、基于差分进化的BBO（DE/BBO）算法的实验结果表明,改进算法提高了算法的全局搜索能力、收敛速度和解的精度。     

Biogeography-based optimization for constrained optimization problems

Biogeography-based optimization (BBO) has been recently proposed as a viable stochastic optimization algorithm and it has so far been successfully applied in a variety of fields, especially for unconstrained optimization problems. The present paper shows how BBO can be applied for constrained optimization problems, where the objective is to find a solution for a given objective function, subject to both inequality and equality constraints.     

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.     

Two-stage update biogeography-based optimization using differential evolution algorithm (DBBO)

The present paper proposes a new stochastic optimization algorithm as a hybridization of a relatively recent stochastic optimization algorithm, called biogeography-based optimization (BBO) with the differential evolution (DE) algorithm. This combination incorporates DE algorithm into the optimization procedure of BBO with an attempt to incorporate diversity to overcome stagnation at local optima. We also propose to implement an additional selection procedure for BBO, which preserves fitter habitats for subsequent generations. The proposed variation of BBO, named DBBO, is tested for several benchmark function optimization problems. The results show that DBBO can significantly outperform the basic BBO algorithm and can mostly emerge as the best solution providing algorithm among competing BBO and DE algorithms.     

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.     

生物地理学优化算法理论及其应用研究综述

生物地理学优化算法（Biogeography-Based Optimization,BBO）是Simon提出的一种基于生物地理学理论的新型智能优化算法,具有良好的收敛性和稳定性。从BBO算法提出的背景出发,介绍了算法的基本理论、算法特点以及算法流程。总结了BBO算法的研究进展,包括BBO算法的理论分析、算法的改进、算法与其他优化算法的混合算法以及BBO算法在函数优化、电力系统、图像处理、机器人路径规划以及调度优化等领域的典型应用。对BBO算法有待解决的问题和未来研究方向进行了总结。     

An analysis of the equilibrium of migration models for biogeography-based optimization

Motivated by the migration mechanisms of ecosystems, various extensions to biogeography-based optimization (BBO) are proposed here. As a global optimization method, BBO is an original algorithm based on the mathematical model of organism distribution in biological systems. BBO is an evolutionary process that achieves information sharing by biogeography-based migration operators. In BBO, habitats represent candidate problem solutions, and species migration represents the sharing of features between candidate solutions according to the fitness of the habitats. This paper generalizes equilibrium species count results in biogeography theory, explores the behavior of six different migration models in BBO, and investigates performance through 23 benchmark functions with a wide range of dimensions and diverse complexities. The performance study shows that sinusoidal migration curves provide the best performance among the six different models that we explored. In addition, comparison with other biology-based optimization algorithms is investigated, and the influence of the population size, problem dimension, mutation rate, and maximum migration rate of BBO are also studied.     

Fruit classification by biogeography‐based optimization and feedforward neural network

Accurate fruit classification is difficult to accomplish because of the similarities among the various categories. In this paper, we proposed a novel fruit‐classification system, with the goal of recognizing fruits in a more efficient way. Our methodology included the following steps. First, a four‐step pre‐processing was employed. Second, the features (colour, shape, and texture) were extracted. Third, we utilized principal component analysis to remove excessive features. Fourth, a novel fruit‐classification system based on biogeography‐based optimization (BBO) and feedforward neural network (FNN) was proposed, with the short name of BBO‐FNN. The experiment employed over 1653 chromatic fruit images (18 categories) by fivefold stratified cross‐validation. The results showed that the proposed BBO‐FNN yielded an overall accuracy of 89.11%, which was higher than the five state‐of‐the‐art methods: genetic algorithm‐FNN, artificial bee colony‐FNN, particle swarm optimization‐FNN, kernel support vector machine, and ant colony optimization‐FNN. Also, the BBO‐FNN achieved the same accuracy as fitness‐scaling chaotic artificial bee colony‐FNN, but it performed much faster than the latter. The proposed BBO‐FNN was effective in fruit‐classification in terms of classification accuracy and computation time. This indicated that it can be applied in credible use.     

生物地理学优化算法研究

对生物地理学优化算法(biogeography-based optimization,BBO)的研究现状进行了总结,并介绍了一些最新的研究进展。从BBO算法提出的背景出发,讨论了算法的主要思想、算法原理以及算法步骤。论述了该算法的研究进展,包括BBO算法的改进、算法的收敛性分析、BBO算法与其他算法的融合以及BBO算法在优化领域的典型应用,对BBO算法有待研究的问题做了总结。     

Handling multiple objectives with biogeography-based optimization

Biogeography-based optimization (BBO) is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization (BBMO) is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability effciently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II (NSGA-II) and archive-based micro genetic algorithm (AMGA) in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.     

Biogeography based hybrid scheme for automatic detection of epileptic seizures from EEG signatures

Biogeography Based Optimization (BBO) algorithm is one of the nature-inspired optimization methods, based on the study of geographical distribution of species on earth. The present research work is based upon decomposition of human electroencephalograms (EEG) signal by Wavelet Packet Transform, computation of a complete feature set using statistical and non-statistical properties followed by optimal selection of features by BBO; the optimality criterion being classification rate. The stopping criterion for BBO is set to 100% correct classification rate. The proposed algorithm is novel in terms of TWSVM computing the Habitat Suitability Index (HSI) values for BBO, perfect classification rate, low computation time, and feature selection mechanism. The proposed scheme outperforms several previous results reported in literature in that it gives a feature subset which gives 100% classification accuracy for different classification instances.     

生物地理学优化算法中迁移算子的比较

生物地理学优化算法(Biogeography-Based Optimization,BBO)是一种模仿物种迁移规律的智能优化算法,其中迁移算子是影响优化效果的关键环节.基于迁移地的选择模式(以迁出率高的栖息地为主导或者以迁入率高的栖息地为主导)和迁移量的规模(单变量和部分变量),提出了BBO算法中可能存在的四种迁移方式.通过对13个经典实例的实验仿真,比较4种迁移算子的优化结果,阐明了产生差异的原因.实验结果表明,迁入主导的部分迁移式算子优化效果最好.     

一种改进的生物地理学优化算法

生物地理学优化算法（BBO）作为一种新型的智能算法,在其提出不到十年的时间内受到学界的广泛关注和研究,并显示出了广阔的应用前景。为了提高算法的优化性能,对BBO算法提出一种改进,该算法在将差分优化算法（DE）中的局部搜索策略同BBO算法中的迁移策略相结合的基础上,针对迁移算子和变异算子分别进行改进,提出了二重迁移算子和二重变异算子,使得栖息地个体在进化过程中得到更高的进化概率,从而使得算法的寻优能力得到进一步提升。通过6个高维函数的测试,结果表明该算法在优化高维优化问题时,较其他几种生物地理学优化算法具有更好的收敛性和稳定性。     

Water wave optimization: A new nature-inspired metaheuristic

Nature-inspired computing has been a hot topic in scientific and engineering fields in recent years. Inspired by the shallow water wave theory, the paper presents a novel metaheuristic method, named water wave optimization (WWO), for global optimization problems. We show how the beautiful phenomena of water waves, such as propagation, refraction, and breaking, can be used to derive effective mechanisms for searching in a high-dimensional solution space. In general, the algorithmic framework of WWO is simple, and easy to implement with a small-size population and only a few control parameters. We have tested WWO on a diverse set of benchmark problems, and applied WWO to a real-world high-speed train scheduling problem in China. The computational results demonstrate that WWO is very competitive with state-of-the-art evolutionary algorithms including invasive weed optimization (IWO), biogeography-based optimization (BBO), bat algorithm (BA), etc. The new metaheuristic is expected to have wide applications in real-world engineering optimization problems.     

引入物种演化的改进生物地理学优化算法

针对生物地理学优化算法(biogeography-based optimization, BBO)易早熟收敛、陷入局部最优的问题,引入物种演化理论提出了改进生物地理学优化算法。该算法将所有栖息地按照物种数量划分为三种地区,并建立协同进化关系,合理地采用区间入侵、区内合作/竞争策略,满足多样性的同时避免了早熟收敛。定义了物种更迭和物种进化两种变异策略,提出的双策略协同变异算子旨在解决变异算子对较优解的破坏。通过CEC2017中的八个基准测试函数与标准BBO及相关改进算法相比,该算法在算法性能、稳定性等方面优于BBO及其他改进算法,且该算法不易被局部最优值所限制。将该算法应用于以最大完工时间为目标的柔性作业车间调度问题(flexible Job-Shop scheduling problem, FJSP)以检验其实际应用价值,实验表明,该算法在解决FJSP上具有一定的有效性。     

Emergency railway wagon scheduling by hybrid biogeography-based optimization

Railway transportation plays an important role in many disaster relief and other emergency supply chains. Based on the analysis of several recent disaster rescue operations in China, the paper proposes a mathematical model for emergency railway wagon scheduling, which considers multiple target stations requiring relief supplies, source stations for providing supplies, and central stations for allocating railway wagons. Under the emergency environment, the aim of the problem is to minimize the weighted time for delivering all the required supplies to the targets. For efficiently solving the problem, we develop a new hybrid biogeography-based optimization (BBO) algorithm, which uses a local ring topology of population to avoid premature convergence, includes the differential evolution (DE) mutation operator to perform effective exploration, and takes some problem-specific mechanisms for fine-tuning the search process and handling the constraints. Computational experiments show that our algorithm is robust and scalable, and outperforms some state-of-the-art heuristic algorithms on a set of problem instances.     

基于差分进化的生物地理学优化算法

针对生物地理学优化算法在实数编码时搜索能力较弱的缺点,提出一种基于差分进化的混合优化算法(BBO/DEs)。通过将差分进化的搜索性与生物地理优化算法的利用性有机结合,以解决原算法在局部搜索时容易出现早熟的问题;并构造一种基于Levy分布的变异方式,确保种群在进化过程中保持多样性;最后通过实验比较,选取了合适的试验策略。利用高维标准测试函数对相关算法进行实验,结果表明该算法能够克服搜索能力不足的缺点,并继承了原算法的快速收敛性能,可以有效兼顾精度与速度的要求。     

Range-free 3D node localization in anisotropic wireless sensor networks

In this paper, we propose two computationally efficient ‘range-free’ 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.