Performance Of Biogeography-based Optimization 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.

Enhancing the Performance of Biogeography-Based Optimization Using Multitopology and Quantitative Orthogonal Learning

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.

Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization

This paper explores the performance of biogeography-based optimization (BBO) algorithm for solving combined heat and power dispatch (CHPD) or cogeneration problem of power system. BBO is a type of evolutionary algorithm which is based on the theory of biogeography and is inspired by the two concepts, namely migration of species between “islands” via flotsam, wind, flying, swimming, etc. and mutation. To improve the convergence property and solution quality, blended crossover strategy of genetic algorithm (GA) is integrated with conventional BBO algorithm in this study. The effect of valve-point in cost function is considered by adding an absolute sinusoidal term with the conventional polynomial cost function. The potential of the proposed BBO and GA based BBO (GABBO) algorithms are assessed by means of an extensive comparative study of the solutions obtained for small and medium CHPD problems of power systems. To show the priority of the proposed algorithm, comparative studies are carried out to examine the effectiveness of the proposed BBO and GABBO approaches over evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO) and time varying acceleration coefficients PSO (TVAC-PSO reported in the literature. The experimental results and comparison with other algorithms demonstrate that the proposed GABBO algorithm can be a proficient substitute lying on solving combined heat and power dispatch problems.

Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network

Classification of experimental datasets such as target and clutter in sonar applications is a complex and challenging problem. One of the most useful instrument to classify sonar datasets is Multi-Layer Perceptron Neural Network (MLP NN). In this paper, due to the optimally updating the weights and biases vector of the MLP NN, Biogeography-Based Optimization (BBO) is used to train the network. BBO has a fair ability to solve high-dimensional real-world problems (such as sonar dataset classification) by maintaining a suitable balance between exploration and exploitation phases. The performance of BBO is sensitive to the migration model, especially for high-dimensional problems. To improve the exploitation ability of BBO and to record the better results for classifying sonar dataset, we propose novel migration models such as exponential-logarithmic, and some improved migration models having different emigration and immigration mathematical functions. To validate the performance of the proposed classifiers, this network will classify three datasets with various sizes and complexities. The simulation results indicate that our newly proposed classifiers perform better than the other benchmark algorithms in addition to original BBO in terms of avoiding gets stuck in local minima, classification accuracy, and convergence speed.

A novel disruption in biogeography-based optimization with application to optimal power flow problem

Biogeography-based optimization (BBO) is an emerging meta-heuristic algorithm. Due to ease of implementation and very few user-dependent parameters, BBO gained popularity among researchers. The performance of BBO is highly dependent on its two operators, migration and mutation. The performance of BBO can be significantly improved by either modifying these operators or by introducing a new operator into it. This paper proposes a new operator, namely the disruption operator to improve the capability of exploration and exploitation in BBO. The proposed DisruptBBO (DBBO) has been tested on well-known benchmark problems and compared with various versions of BBO and other state-of-the-art metaheuristics. The experimental results and statistical analyses confirm the superior performance of the proposed DBBO in solving various nonlinear complex optimization problems. The proposed algorithm has also been applied to the optimal power flow optimization problem from the electrical engineering background.

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.

Hybrid Biogeography-Based Optimization for Solving Tomato Planting Planning

How to solve the unbalanced supply of raw tomato is a difficult problem which puzzles ketchup factory for a long period of time. Biogeography-based optimization (BBO) is proposed to solve tomato planting planning problem. Nonlinear mathematical model of tomato planting planning is constructed. Some strategies, such as cosine migration model, disturbing migration operator, mutation operator based on Gaussian distribution, and the reasonable combination of mutation operator of differential evolution algorithm (DE) and chaos technology, were used to improve the exploitation and exploration performance of BBO. The problem of planting planning is transformed into the combination optimization problem. We take a Ketchup factory of Xinjiang in China as an example. The simulation results show that the proposed algorithm can meet the balance between raw tomato material production and Ketchup factory capacity. And the proposed algorithm compares with other intelligent optimization algorithms. It verifies rationality of the presented mathematical model and good astringency and feasibility of the proposed algorithm. Thus, optimal planting planning for tomato planting can be achieved.

Hybrid biogeography-based optimization for integer programming.

Biogeography-based optimization (BBO) is a relatively new bioinspired heuristic for global optimization based on the mathematical models of biogeography. By investigating the applicability and performance of BBO for integer programming, we find that the original BBO algorithm does not perform well on a set of benchmark integer programming problems. Thus we modify the mutation operator and/or the neighborhood structure of the algorithm, resulting in three new BBO-based methods, named BlendBBO, BBO_DE, and LBBO_LDE, respectively. Computational experiments show that these methods are competitive approaches to solve integer programming problems, and the LBBO_LDE shows the best performance on the benchmark problems.

A Comprehensive Comparison of the Original Forms of Biogeography-Based Optimization Algorithms

Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and one of metaheuristic algorithms. This technique is based on an old mathematical study that explains the geographical distribution of biological organisms. The first original form of BBO was introduced in 2008 and known as a partial migration based BBO. After three months, BBO was re-introduced again with additional three other forms and known as single, simplified partial, and simplified single migration based BBOs. Then a lot of modifications and hybridizations were employed to boost-up the performance of BBO and solve its weak exploration. However, the literature lacks the explanations and the reasons on which the modifications of the BBO forms are based on. This paper tries to clarify this issue by making a comparison between the four original BBO algorithms through 23 benchmark functions with different dimensions and complexities. The final judgment is confirmed by evaluating the performance based on the effect of the problem’s dimensions, the side constraints and the population size. The results show that both single and simplified single migration based BBOs are faster, but have less performance as compared to the others. The comparison between the partial and the simplified partial migration based BBOs shows that the preference depends on the population size, problem’s complexity and dimensions, and the values of the upper and lower side constraints. The partial migration model wins when these factors, except the population size, are increased, and vice versa for the simplified partial migration model. The results can be used as a foundation and a first step of modification for enhancing any proposed modification on BBO including the existing modifications that are described in 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.

Accelerated biogeography-based optimization with neighborhood search for optimization

Biogeography-based optimization (BBO) inherently lacks exploration capability that leads to slow convergence. To address this limitation, authors present a memetic algorithm (MA) named as aBBOmDE, which is a new variant of BBO. In aBBOmDE, the performance of BBO is accelerated with the help of a modified mutation and clear duplicate operators. Then modified DE (mDE) is embedded as a neighborhood search operator to improve the fitness from a predefined threshold. mDE is used with mutation operator DE/best/1/bin to explore the search near the best solution. The length of local search is a choice that balances between the search capability and the computational cost. In aBBOmDE, migration mechanism is kept same as that of BBO in order to maintain its exploitation ability. Modified operators are utilized to enhance the exploration ability while a neighborhood search operator further enhances the search capability of the algorithm. This combination significantly improves the convergence characteristics of the original algorithm. Extensive experiments have been carried out on forty benchmark functions to show the effectiveness of the proposed algorithm. The results have been compared with original BBO, DE, CMAES, other MA and DE/BBO, a hybrid version of DE and BBO. aBBOmDE is also applied to compute patch dimensions of rectangular microstrip patch antennas (MSAs) with various substrate thicknesses so as to be used a CAD formula for antenna design.

Integrating Chaos to Biogeography-Based Optimization Algorithm

This work integrates chaotic maps into the recently proposed heuristic algorithm called Biogeography-Based Optimization (BBO) algorithm. The effects of 3 different chaotic maps such as Circle, Sine, and Sinusoidal on improving the performance of BBO are investigated in terms of local optima avoidance and convergence speed. The algorithms are benchmarked on four thirty-dimensional test function such as Sphere, Schwefel, Rasterigin, and Griewank. The results prove that chaotic maps (especially Sine map) are able to improve the performance of BBO.

Neighborhood Search-Driven Accelerated Biogeography-Based Optimization for Optimal Load Dispatch

Lack of exploration capability of biogeography-based optimization (BBO) leads to slow convergence. To address this limitation, this paper presents a memetic algorithm (MA), namely, aBBOmDE, which is a new version of BBO to solve both complex and noncomplex economic load dispatch (ELD) problems of thermal plant. In aBBOmDE, the performance of BBO is accelerated by using a modified mutation and clear duplicate operators. Then, modified DE (mDE) is embedded as a neighborhood search operator to improve their fitness after a predefined threshold. mDE is used with mutation operator DE/best/1/bin to explore the search near the best solution. The length of local search is set to achieve a balance between the search capability and the excess computational cost. In aBBOmDE, migration mechanism is kept same as that of BBO to maintain its exploitation ability. Modified operators are utilized to enhance the exploration ability while a neighborhood search operator, further, enhances the search capability of the algorithm. This combination significantly improves the convergence characteristics of the original algorithm. The effectiveness of the proposed algorithm has been verified on five different test systems with varying degree of complexity. The results have been compared with other existing techniques. The results indicate that the proposed approach can efficiently solve practical ELD problems.

A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem

Biogeography-based optimization (BBO) algorithm is a new kind 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. In this paper, the BBO algorithm is developed for flexible job shop scheduling problem (FJSP). It means that migration operators of BBO are developed for searching a solution area of FJSP and finding the optimum or near-optimum solution to this problem. In fact, the main aim of this paper was to provide a new way for BBO to solve scheduling problems. To assess the performance of BBO, it is also compared with a genetic algorithm that has the most similarity with the proposed BBO. This similarity causes the impact of different neighborhood structures being minimized and the differences among the algorithms being just due to their search quality. Finally, to evaluate the distinctions of the two algorithms much more elaborately, they are implemented on three different objective functions named makespan, critical machine work load, and total work load of machines. BBO is also compared with some famous algorithms in the literature.

Biogeography-Based Optimization

Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems. Just as the mathematics of biological genetics inspired the development of genetic algorithms (GAs), and the mathematics of biological neurons inspired the development of artificial neural networks, this paper considers the mathematics of biogeography as the basis for the development of a new field: biogeography-based optimization (BBO). We discuss natural biogeography and its mathematics, and then discuss how it can be used to solve optimization problems. We see that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO). This makes BBO applicable to many of the same types of problems that GAs and PSO are used for, namely, high-dimension problems with multiple local optima. However, BBO also has some features that are unique among biology-based optimization methods. We demonstrate the performance of BBO on a set of 14 standard benchmarks and compare it with seven other biology-based optimization algorithms. We also demonstrate BBO on a real-world sensor selection problem for aircraft engine health estimation.