Original Biogeography-based Optimization Research Articles

Blockchain-Based Optimization Model for Evaluating Psychological Mental Disease and Mental Fitness.

The current work describes a blockchain-based optimization approach that mimics the psychological mental illness evaluation procedure and evaluates mental fitness. Combining lightweight models with blockchains can give a variety of benefits in the healthcare business. This study aims to offer an improved review and learning optimization technique (SPLBO) based on the social psychology theory to overcome the biogeography-based optimization (BBO) algorithm's shortcomings of low optimization accuracy and instability. It also creates high-accuracy solutions in recognized domains quickly. To retain student individuality, students can be divided into two groups: Human psychological variables are incorporated in the algorithm's improvement: in the “teaching” step of the original BBO algorithm; the “expectation effect” theory of social psychology is combined: “field-independent” and “field-dependent” cognitive styles. As a consequence, low-weight deep neural networks have been designed in such a manner that they require fewer resources for optimal design while also improving quality. A responsive student update component is also introduced to duplicate the effect of the environment on students' learning efficiency, increase the method's global search capabilities, and avoid the problem of falling into a local optimum in the first repetition.

A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration

Medical images acquired from different modalities give rise to many practical problems in image registration. Intensity-based registration techniques have been increasingly used in multimodal image registration; these techniques integrate different images that have shared content into a single representation, by transformation. The estimation of the optimal transformation requires the optimization of a similarity metric between the images. Recently, many optimization methods have been proposed that focus on the development of the optimization component. However, there is still room for large amounts of improvement, from both an efficiency point of view and a quality perspective. In this paper we present a new Biogeography-based Optimization (BBO) algorithm, the Biogeography-based Optimization algorithm with Elite Learning (BBO-EL), for multimodal medical image registration. First, we propose a hybrid full migration operator in which each individual has the chance to perform the migration operation and the whole population has the chance to expand the search space. In this way, the search ability of the BBO algorithm is enhanced and matches well the characteristics of multimodal medical image registration. In addition, considering that the quality of some individuals could be deteriorated as caused by the migration operation, we propose an undo operator on the deteriorated individuals. Thus, the lower bound of the whole population’s quality can be maintained at a higher level. Furthermore, in the original BBO algorithm, a number of good individuals might be not involved in the migration operation, and we present an elite learning operator that is based on social comparison theory to improve the upper bound of the whole population’s quality. Therefore, after improving both the lower bound and the upper bound of the whole population’s quality, the accuracy and the convergence speed of the multimodal medical registration can be greatly enhanced. The BBO-EL has been tested in many experiments on benchmark datasets include six kind of different modality images, from up to eighteen different patients, which can make up 54 multimodal registration scenarios. The BBO-EL obtained 30 best performance scenarios while the state-of-the-art algorithm obtained 21 scenarios. The results demonstrated that BBO-EL outperforms the state-of-the-art algorithm in most cases for practical problems.

Solving Traveling Salesman Problem based on Biogeography-based Optimization and Edge Assembly Cross-over

Biogeography-Based Optimization (BBO) algorithm has recently been of great interest to researchers for simplicity of implementation, efficiency, and the low number of parameters. The BBO Algorithm in optimization problems is one of the new algorithms which have been developed based on the biogeography concept. This algorithm uses the idea of animal migration to find suitable habitats for solving optimization problems. The BBO algorithm has three principal operators called migration, mutation and elite selection. The migration operator plays a very important role in sharing information among the candidate habitats. The original BBO algorithm, due to its poor exploration and exploitation, sometimes does not perform desirable results. On the other hand, the Edge Assembly Crossover (EAX) has been one of the high power crossovers for acquiring offspring and it increased the diversity of the population. The combination of biogeography-based optimization algorithm and EAX can provide high efficiency in solving optimization problems, including the traveling salesman problem (TSP). This paper proposed a combination of those approaches to solve traveling salesman problem. The new hybrid approach was examined with standard datasets for TSP in TSPLIB. In the experiments, the performance of the proposed approach was better than the original BBO and four others widely used metaheuristics algorithms.

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.

Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network

Multi-Layer Perceptron Neural Network (MLP NN) is one of the most applicable tools in solving complicated problems as well as classifying between target and non-target in sonar applications. In this paper, we use Biogeography-based Optimization (BBO) to train the MLP NN. Due to improving the exploration ability and enhancing the diversity of the population, we propose mutation operators into BBO and call it Neighborhood Search Trainer (NST). In addition, these operators prepare more balance between exploration and exploitation ability of BBO and induce it to record best results for solving high-dimensional problems. To assess the performance of the proposed classifier, this network will be evaluated with three datasets with various sizes and complexities. The results are compared with some of the most popular meta-heuristic algorithms for verification. The simulation results show that the new classifier performs better than the other benchmark algorithms and also than original BBO in terms of avoidance trapping in local optima, classification accuracy, and convergence speed. This paper also implements the designed classifier on the FPGA substrate for testing the real-time application of the proposed method.

Design of wind farm layout with non-uniform turbines using fitness difference based BBO

Biogeography-based optimization (BBO) is an emerging meta-heuristic algorithm. BBO is inspired from the migration of species from one island to another. This study presents the solution of the wind farm layout optimization problem with wind turbines having non-uniform hub heights and rotor radii using BBO and an improved version of BBO. This study proposes an improved version of BBO, Fitness Difference Based BBO (FD-BBO). FD-BBO is obtained by incorporating the concept of fitness differences in original BBO. First, in order to justify the superiority of FD-BBO over BBO, it is tested over 15 standard test problems of optimization. The numerical results of FD-BBO are compared with the original version of BBO and an advanced version of BBO, Blended BBO (BBBO). Through graphical and statistical analyses, FD-BBO is established to be an efficient and accurate algorithm. The BBO, BBBO and FD-BBO are than applied to solve the wind farm layout optimization problem. In the considered problem, not only the location of the wind turbines but hub heights and rotor radii are also taken as decision variables. Two cases of the problems are dealt: 26 turbines in the farm size of 2000 m ×2000 m and 30 turbines in the farm size of 2000 m ×2000 m. Numerical results are compared with earlier published results and that of original BBO and Blended BBO. It is found that FD-BBO is the better approach to solving the problem under consideration.

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.

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

Biogeography-based optimization (BBO) is a competitive population optimization algorithm based on biogeography theory with inherently insufficient exploration capability and slow convergence speed. To overcome limitations, we propose an improved variant of BBO, named PRBBO, for solving global optimization problems. In PRBBO, a hybrid migration operator with random ring topology, a modified mutation operator, and a self-adaptive Powell's method are rational integrated together. The hybrid migration operator with random ring topology, denoted as RMO, is created by using local ring topology to replace global topology, which can avoid the asymmetrical migration operation and enhance potential population diversity. The self-adaptive Powell's method is amended by using self-adaptive parameters for suiting evolution process to enhance solution precision quickly. Extensive experimental tests are carried out on 24 benchmark functions to show effectiveness of the proposed algorithm. Simulation results were compared with original BBO, ABC, DE, other variants of the BBO, and other state-of-the-art evolutionary algorithms. Finally, the effectiveness of operators on the performance of PRBBO is also discussed.

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.

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.

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.

Hybridization of Biogeography-Based

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

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.

Modified Biogeography-Based Optimization with Local Search Mechanism

Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability. To address this limitation, we proposed a modified BBO with local search mechanism (denoted as MLBBO). In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Then, a local search mechanism is used in BBO to supplement with modified migration operator. Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm. The simulation results have been compared with original BBO, DE, improved BBO algorithms, and other evolutionary algorithms. Finally, the performance of the modified migration operator and local search mechanism are also discussed.

Variations of biogeography-based optimization and Markov analysis

Biogeography-based optimization (BBO) is a new evolutionary algorithm that is inspired by biogeography. Previous work has shown that BBO is a competitive optimization algorithm, and it demonstrates good performance on various benchmark functions and real-world optimization problems. Motivated by biogeography theory and previous results, three variations of BBO migration are introduced in this paper. We refer to the original BBO algorithm as partial immigration-based BBO. The new BBO variations that we propose are called total immigration-based BBO, partial emigration-based BBO, and total emigration-based BBO. Their corresponding Markov chain models are also derived based on a previously-derived BBO Markov model. The optimization performance of these BBO variations is analyzed, and new theoretical results that are confirmed with simulation results are obtained. Theoretical results show that total emigration-based BBO and partial emigration-based BBO perform the best for three-bit unimodal problems, partial immigration-based BBO performs the best for three-bit deceptive problems, and all these BBO variations have similar results for three-bit multimodal problems. Performance comparison is further investigated on benchmark functions with a wide range of dimensions and complexities. Benchmark results show that emigration-based BBO performs the best for unimodal problems, and immigration-based BBO performs the best for multimodal problems. In addition, BBO is compared with a stud genetic algorithm (SGA), standard particle swarm optimization (SPSO 07), and adaptive differential evolution (ADE) on real-world optimization problems. The numerical results demonstrate that BBO outperforms SGA and SPSO 07, and performs similarly to ADE for the real-world problems.

Extrapolated Biogeography-Based Optimization (eBBO) for Global Numerical Optimization and Microstrip Patch Antenna Design

Biogeography-Based Optimization (BBO) uses the idea of probabilistically sharing features between solutions based on the solutions’ fitness values. Therefore, its exploitation ability is good but it lacks in exploration ability. In this paper, the authors extend the original BBO and propose a hybrid version combined with ePSO (particle swarm optimization with extrapolation technique), namely eBBO, for unconstrained global numerical optimization problems in the continuous domain. eBBO combines the exploitation ability of BBO with the exploration ability of ePSO effectively, which can generate global optimum solutions. To validate the performance of eBBO, experiments have been conducted on 23 standard benchmark problems with a range of dimensions and diverse complexities and compared with original BBO and other versions of BBO in terms of the quality of the final solution and the convergence rate. Influence of population size and scalability study is also considered and results are compared with statistical paired t-test. Experimental analysis indicates that the proposed approach is effective and efficient and improves the exploration ability of BBO.

A real-coded biogeography-based optimization with mutation

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm for global optimization. There are some open research questions that need to be addressed for BBO. In this paper, we extend the original BBO and present a real-coded BBO approach, referred to as RCBBO, for the global optimization problems in the continuous domain. Furthermore, in order to improve the diversity of the population and enhance the exploration ability of RCBBO, the mutation operator is integrated into RCBBO. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. The results indicate the good performance of the proposed RCBBO method. Moreover, experimental results also show that the mutation operator can improve the performance of RCBBO effectively.