Hybrid Biogeography-based Optimization Research Articles

Towards sustainable use of foundry by-products: Evaluating the compressive strength of green concrete containing waste foundry sand using hybrid biogeography-based optimization with artificial neural networks

Waste foundry sand (WFS) is known as the main waste material of foundry industries, and its disposal cost and environmental threats have become one of the major challenges in many countries. To promote the reuse of WFS, it can be utilized as a partial substitute for fine aggregates in concrete technology, which reduces disposal costs, adverse environmental effects, and the need to extract natural aggregates. The aim of this study is to predict the compressive strength of concrete containing WFS (CCWFS) using an innovative hybrid technique i.e. biogeography-based optimization (BBO) assisted by an artificial neural network (ANN) as an affordable alternative to minimize the reliance on experimental activities. To develop the proposed model, a comprehensive database (340 mix designs) including effective parameters on the compressive strength of CCWFS is compiled from the open literature. The architectural structure of the proposed model and its performance evaluation were analyzed by a variety of statistical indicators. The results indicated the potential for reliable performance of the proposed BBO-ANN model in predicting the compressive strength of CCWFS. Finally, to validate the proposed model, a comparison was made with results of five recent studies with different prediction models, in which our findings indicate that the proposed BBO-ANN model offers by far the best performance in predicting the compressive strength of CCWFS compared to the other ones. This study provides new insights into applying the hybrid predictive technique that can help future research to predict properties of green concrete containing waste materials more accurately.

A hybrid biogeography-based optimization algorithm to solve high-dimensional optimization problems and real-world engineering problems

According to our extensive investigation, Biogeography-based optimization (BBO) and its variants have not been applied to solve high-dimensional optimization problems. To make a breakthrough in this field, a new BBO variant with hybrid migration operator and feedback differential evolution mechanism, HFBBO, is proposed. Firstly, the example learning method is used to ensure the inferior solutions cannot destroy the superior solutions. Secondly, the hybrid migration operator is presented to balance the exploration and exploitation. It enables the algorithm to switch freely between local search and global search. Finally, the feedback differential evolution mechanism is designed to replace the random mutation operator. HFBBO can select the mutation mode intelligently by this mechanism to avoid getting stuck in local optima. Meanwhile, the Markov model is established to prove the convergence of HFBBO, and the complexity is also discussed. A series experiments are carried out on 24 benchmark functions, CEC2017 test suite and 12 real-world engineering problems. The results of the Wilcoxon’s rank-sum test and Friedman’s test show that HFBBO has better competitiveness and stability than the 27 compared algorithms. Furtherly, the performance of HFBBO is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. Experimental results show that this method can effectively solve high-dimensional optimization problems.

A hybrid biogeography-based optimization algorithm for three-dimensional bin size designing and packing problem

The three-dimensional bin packing problem (3D-BPP) plays a critical role in logistics activities. Prior studies focus on 3D-BPP in cases where bin size is predetermined. However, bin volume utilization can be improved significantly by optimizing bin size. In this study, we propose a novel three-dimensional bin size designing and packing problem (3D-BSDPP) and a hybrid biogeography-based optimization (HBBO) algorithm to address the problem by maximizing bin volume utilization by optimizing the bin size. The HBBO combines a differential evolution algorithm with basic biogeography-based optimization to improve exploration ability, and a novel local search strategy involving three specific local search operators is proposed to accelerate the HBBO convergence. Finally, a novel heuristic packing method is employed to find a high-quality packing solution. The proposed algorithm is evaluated through experiments carried out on a practical bin packing application. The results indicate that the proposed algorithm outperforms existing algorithms in solving the 3D-BSDPP.

Fault diagnosis of electric rudder based on self-organizing differential hybrid biogeography algorithm optimized neural network

Electric rudder is one of the most essential equipment on an aircraft. The diagnosis of electric rudder faults is of great significance to the production of electric rudder. To this end, this paper proposes a fault diagnosis method based on back-Propagation neural network (BPNN), which optimizes the weights and biases of BPNN using the self-organizing differential hybrid biogeography-based optimization algorithm (SODEBBO), and completes the automatic classification of multiple faults in electric rudder testing. The self-organizing differential hybrid biogeography-based optimization algorithm optimized the BPNN (SODEBBO-BP) model is compared with other eight models, and the results show that the maximum accuracy of SODEBBO-BP algorithm is 0.9918, the precision is 0.9981, F1-Score is 0.9981, G-mean is 0.9853, and Kappa is 0.9643 compared with other eight models. The model proposed in this paper is validated by experimental study and can be applied to the production and maintenance of electric rudder in the future.

Developing a Fuzzy Green Supply Chain Management Problem Considering Location Allocation Routing Problem: Hybrid Meta-Heuristic Approach

Nowadays, the internationalization of supply chains makes the management of operation affairs face a great challenge. On the other hand, vague parameters have challenged decision-makers to drive decision-making. To cope with these challenges, this study tries to model a green SCM (GSCM) model which considers fuzzy parameters. The objective function of our model is to minimize total fuzzy cost including fuzzy establishment costs of the plants and distribution centers, fuzzy transportation costs among the suppliers, facilities, and customers, fuzzy hiring cost of the transportation facilities, and miscellaneous fuzzy environmental impact costs. The developed model also includes facilities location constraints, material flow constraints, open transportation routing from plants to customers and from distribution centers to customers. Also, determining alternative products for customers has not been addressed in the literature. Therefore, this paper tries to focus on the mentioned complex problem and develop a comprehensive model. Because of the level of complexity of the developed model, two empowered meta-heuristic approaches, named fuzzy hybrid genetic algorithm (FHGA) and fuzzy hybrid biogeography-based optimization algorithm (FHBBO), are implemented to solve the NP-hard developed problem. According to the best of our knowledge, the proposed FHGA is not addressed in the literature in this way. For instance, most of the fuzzy algorithms either are not hybrid or get out of the fuzzy environment in one of their complex evolution processes. However, our fuzzy hybrid algorithms follow a fuzzy environment from beginning test initialization to calculating the objective function and presenting the convergence plots and none of our parameters are defuzzied in all steps of these processes. Besides, miscellaneous Figures, illustrations, and tables support the explanations of results.

Scheduling the covering delivery problem in last mile delivery

This study proposes a covering delivery problem (CDP), which has practical applications in last mile parcel delivery in online shopping. A set of vertices are considered, including depot, facilities, customers that must be covered, customers that must be visited, and customers that can be visited or covered. The goal is to construct a Hamiltonian cycle with minimum total costs over a subset of vertices, in which each customer is visited by the cycle or covered by a facility on the cycle. Flow-based and bilevel programming models are proposed to formulate the problem. Essentially, the bilevel programming formulations consist of one leader and one follower subproblems. On the basis of the bilevel programming ideas, a hybrid biogeography-based optimization (HBBO) is developed to solve the problem. In HBBO, BBO and 3-opt are used to solve the leader and follower problems, respectively. Lastly, computational experiments on a set of randomly generated and benchmark instances indicate the effectiveness of the proposed method.

Hybrid Biogeography-Based Optimization and Genetic Algorithm for Feature Selection in Mammographic Breast Density Classification

It can be acknowledged from the literature that the high density of breast tissue is a root cause for the escalation of breast cancer among the women, imparting its prime role in Cancer Death among women. Moreover, in this era where computer-aided diagnosis systems have become the right hand of the radiologists, the researchers still find room for improvement in the feature selection techniques. This research aspires to propose hybrid versions of Biogeography-Based Optimization and Genetic Algorithm for feature selection in Breast Density Classification, to get rid of redundant and irrelevant features from the dataset; along with it to achieve the superior classification accuracy or to uphold the same accuracy with lesser number of features. For experimentation, 322 mammogram images from mini-MIAS database are chosen, and then Region of Interests (ROI) of seven different sizes are extracted to extract a set of 45 texture features corresponding to each ROI. Subsequently, the proposed algorithms are used to extract an optimal subset of features from the hefty set of features corresponding to each ROI. The results indicate the outperformance of the proposed algorithms when results were compared with some of the other nature-inspired metaheuristic algorithms using various parameters.

Optimal feature selection for SAR image classification using biogeography-based optimization (BBO), artificial bee colony (ABC) and support vector machine (SVM): a combined approach of optimization and machine learning

Land cover classification is one of the most important applications of POLSAR images. In this paper, a hybrid biogeography-based optimization support vector machine (HBBOSVM) has been introduced to classify POLSAR images of RADARSAT 2 in band C acquired from San Francisco, USA. The main purpose of this classification is to minimize the number of features and maximize classification accuracy. The proposed method consists of three main steps: preprocessing, feature selection and classification. As preprocessing, radiometric calibration, speckle reduction and feature extraction have been performed. In the proposed HBBO, the combination of onlooker bee of artificial bee colony (ABC) and migration operator of biogeography-based optimization has been applied in order to optimal feature selection. Then, SVM has been used to classify the pixels into specific labels of land-covers. The ground truth samples have been generated by google earth image, Pauli RGB image, high resolution image and national land cover database (NLCD 2006). The performance of HBBOSVM has been compared with BBOSVM, ABCSVM, particle swarm optimization support vector machine (PSOSVM) and the results of previous studies. In addition, the performance of HBBO is evaluated upon 20 well-known benchmark problems. According to the obtained results, the overall accuracy and average accuracy of HBBOSVM are 96.01% and 93.37% respectively which is the best result in comparison with other results. The HBBOSVM has better performance than other algorithms in terms of overall accuracy, kappa coefficient, average accuracy, convergence trend, and stability. In addition, the HBBO can be considered as a successful meta-heuristic for benchmark problems. This paper displays that the combined approach of optimization and machine learning methods provides powerful results.

A modified biogeography-based optimization algorithm with improved mutation operator for job shop scheduling problem with time lags

Abstract This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra (minimum and maximum) delays can be introduced between successive operations of the same job. It belongs to a category of problems known as NP-hard problem due to large solution space. Biogeography-based optimization is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon in 2008 to optimize hard combinatorial optimization problems. We propose a hybrid biogeography-based optimization (HBBO) algorithm for solving the job shop scheduling problem with additional time lag constraints with minimization of total completion time. In the proposed HBBO, the effective greedy constructive heuristic is adapted to generate the initial population of habitat. Moreover, a local search metaheuristic is investigated in the mutation step in order to ameliorate the solution quality and enhance the diversity of the population. To assess the performance of HBBO, a series of experiments on well-known benchmark instances for job shop scheduling problem with time lag constraints is performed.

Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem

In recent years, scheduling problems have attracted enormous attentions from practitioners and researches in manufacturing systems, for instance, the scheduling of computing resource in cloud infrastructure and cloud services. The scheduling problems in cloud services, big data and other service-oriented computing problems are regarded as non-separable problems. In this paper, a hybrid biogeography-based optimization with the enhanced mutation operator and CMA-ES (HBBO-CMA) is proposed to enhance the ability of exploitation on non-separable problems and alleviate the rotational variance. In the migration operator, the rotationally invariant migration operator is designed to reduce the dependence of BBO on the coordinate system and control the diversity of population. In the mutation operator, an enhanced mutation operator, which is sampled from the mean value and stand deviation of the variables of population, is employed to effectively escape the local optimum. Furthermore, the CMA-ES, which has outstanding performance on the non-separable problem, is applied to extend the exploitation of HBBO-CMA. Experimental results on CEC-2017 demonstrated the effectiveness of the proposed HBBO-CMA.

An empirical-based rainfall-runoff modelling using optimization technique

ABSTRACTThis study proposes a new hybrid biogeography-based optimization (BBO) technique to achieve a better balance between exploitation and exploration sides of BBO. The proposed hybrid metaheuristic algorithm, namely HBBPSGWO, enhances the exploration ability of BBO by combining it with the exploration side of particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms. The proposed hybrid approach is integrated with two classical machine learning models, namely artificial neural network (ANN) and adaptive neuro-fuzzy system (ANFIS), for 1-day-ahead streamflow prediction in a catchment. Daily rainfall and discharge value from 1979 to 2015 of the catchment Fal at Tregony (United Kingdom) is used to validate the performance efficiency of the proposed hybrid algorithm, the HBBPSGWO along with ANN and ANFIS separately. The results demonstrate that the HBBPSGWO-ANN/HBBPSGWO-ANFIS improves the BBO convergence by avoid the trapping it into local minima and the performance has significantly improved compare to basic BBO-based ANN (BBO-ANN)/BBO-based ANFIS (BBO-ANFIS) and PSO-based ANN (PSO-ANN)/PSO-based ANFIS (PSO-ANFIS). In testing phase, root mean square error (RMSE) of HBBPSGWO-ANN is found lowest (0.901) compare to BBO-ANN (0.949) and PSO-ANN (0.926). Whereas in the case of integrated ANFIS, the RMSE of HBBPSGWO-ANFIS is also found minimum (0.741) compare to BBO-ANFIS (0.805) and PSO-ANFIS (0.828). The finding of this research concludes that the proposed hybrid metaheuristic algorithm has better capability to predict the daily streamflow. Moreover, the convergence of HBBPSGWO requires a smaller number of iterations to run in comparison to BBO and PSO.

Stochastic economic analysis of FACTS devices on contingent transmission networks using hybrid biogeography-based optimization

Flexible AC transmission systems (FACTS) devices have advantages of enhancing AC system controllability and stability, increasing power transfer capability and relieving congestion. Finding the best sizing and siting of them is obligatory to obtain the maximum benefit. In this paper, the optimum planning is done for determining the suitable sizing and siting of the FACTS devices during their lifetime span. The proposed planning approach is implemented to the networks, which are suffering from the contingency to demonstrate that how proper sizing and siting of the FACTS devices are able to deal with the existing problems in such networks. In fact, the maximum social welfare, reducing load shedding cost and construction cost of the new branches besides of the technical issues such as voltage improvement are the main concerns of the present work. To accomplish these aims accurately and reduce the computation time, a hybrid approach, consisting of mathematical and heuristic methods, is proposed for solving the proposed planning problem. The mentioned algorithm is a combination of biogeography-based optimization (BBO) as a heuristic algorithm and nonlinear programming as a mathematical approach. Furthermore, since the stochastic natures of renewable energy sources and load variations contribute to the optimum decisions, the stochastic formulation has been considered in the planning problem using the efficient point estimation ( $$ 2m + 1 $$ ) scheme. Finally, the proposed planning approach is tested on different test systems, namely IEEE 14-bus, IEEE 57-bus and IEEE 300-bus, in order to verify its effectiveness from a different point of views.

Hybrid biogeography-based optimization with shuffled frog leaping algorithm and its application to minimum spanning tree problems

Biogeography-Based Optimization (BBO), a good meta-heuristic optimization algorithm, has drawn much attention and been applied widely to many areas. However, BBO can not do well in solving some complex and diversified optimization problems. In order to obtain an algorithm with better optimization performance, this paper presents a hybrid BBO with Shuffled Frog Leaping Algorithm (SFLA), named HBBOS. Firstly, we improve BBO. BBO's mutation operator is got rid of and its migration operator is improved. Two novel updating mechanisms, i.e. a hybrid cross mechanism and a hybrid disturbance mechanism, are introduced instead of the original migration mechanism to update the immigration habitats' Suitability Index Variables (SIVs) and non-immigration habitats' SIVs, respectively. A differential mechanism is also introduced to prevent the algorithm from falling into local optima to some degree. These improvements can enhance exploration and exploitation and balance them. Secondly, we merge the improved migration operator into SFLA's group structure framework. This can balance exploration and exploitation further. So HBBOS is obtained. HBBOS can effectively maximize the two algorithms' advantages and minimize the defects so that it can obtain better optimization performance. A large number of experiments are made on benchmark functions with various types and complexities, such as a set of classic functions and CEC2014 test set. HBBOS is also applied to minimum spanning tree problems. The experimental results show that HBBOS outperforms quite a few state-of-the-art algorithms. • A hybrid BBO with SFLA, named HBBOS, is proposed to maximize the two algorithms' advantages and minimize their defects. • Hybrid cross and hybrid disturbance mechanisms are introduced into BBO's migration operator to enhance the performance. • A differential mechanism is introduced to prevent the algorithm from falling into local optima to some degree. • The improved migration operator is merged into SFLA's group structure framework to balance exploration and exploitation. • HBBOS can obtain superior performance on benchmark functions and solve the minimum spanning tree problems better.

A hybrid biogeography-based optimization with variable neighborhood search mechanism for no-wait flow shop scheduling problem

The no-wait flow shop scheduling problem (NWFSP) plays an essential role in the manufacturing industry. Inspired by the overall process of biogeography theory, the standard biogeography-based optimization (BBO) was constructed with migration and mutation operators. In this paper, a hybrid biogeography-based optimization with variable neighborhood search (HBV) is implemented for solving the NWFSP with the makespan criterion. The modified NEH and the nearest neighbor mechanism are employed to generate a potential initial population. A hybrid migration operator, which combines the path relink technique and the block-based self-improvement strategy, is designed to accelerate the convergence speed of HBV. The iterated greedy (IG) algorithm is introduced into the mutation operator to obtain a promising solution in exploitation phase. A variable neighbor search strategy, which is based on the block neighborhood structure and the insert neighborhood structure, is designed to perform the local search around the current best solution in each generation. Furthermore, the global convergence performance of the HBV is analyzed with the Markov model. The computational results and comparisons with other state-of-art algorithms based on Taillard and VRF benchmark show that the efficiency and performance of HBV for solving NWFSP.

A novel task scheduling scheme in a cloud computing environment using hybrid biogeography-based optimization

Task scheduling, which plays a crucial role in cloud computing and is the critical factor influencing the performance of cloud computing, is an NP-hard problem that can be solved with a heuristic algorithm. In this paper, we propose a novel heuristic algorithm, called biogeography-based optimization (BBO), and a new hybrid migrating BBO (HMBBO) algorithm, which integrates the migration strategy with particle swarm optimization (PSO). Both methods are proposed to solve the problem of scheduling-directed acyclic graph tasks in a cloud computing environment. The basic idea of our approach is to exploit the advantages of the PSO and BBO algorithms while avoiding their drawbacks. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. Both simulation and real-life experiments are conducted to verify the effectiveness of HMBBO. The experiment shows that compared with several classic heuristic algorithms, HMBBO has advantages in terms of global search ability, fast convergence rate and a high-quality solution, and it provides a new method for task scheduling in cloud computing.

Hybrid biogeography-based optimization with brain storm optimization for non-convex dynamic economic dispatch with valve-point effects

Dynamic economic dispatch (DED), mathematically, is a typical highly complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering valve-point effects. In this paper, a hybrid method named BBOSB by combining biogeography-based optimization (BBO) with brain storm optimization (BSO) is proposed. BBO has good local exploitation ability due to its information sharing mechanism. But it is likely to suffer from premature convergence when dealing with complex multimodal problems. Quite the opposite, BSO possesses excellent global exploration ability owing to its grouping evolution strategy which, however, also can drag its global searching process. In such contexts, the hybrid BBOSB method is able to fully take advantages of both BBO and BSO to conquer premature convergence and to accelerate the global searching process simultaneously. The experimental and comparison results on four non-convex benchmark DED test cases with valve-point effects and a practical provincial power system of China comprehensively demonstrate that BBOSB is highly competitive and can be used as a promising alternative for DED problems. In addition, the effect of population size on the optimization performance is investigated as well.

Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

A Multilayer Perceptron (MLP) is a feedforward neural network model consisting of one or more hidden layers between the input and output layers. MLPs have been successfully applied to solve a wide range of problems in the fields of neuroscience, computational linguistics, and parallel distributed processing. While MLPs are highly successful in solving problems which are not linearly separable, two of the biggest challenges in their development and application are the local-minima problem and the problem of slow convergence under big data challenge. In order to tackle these problems, this study proposes a Hybrid Chaotic Biogeography-Based Optimization (HCBBO) algorithm for training MLPs for big data analysis and processing. Four benchmark datasets are employed to investigate the effectiveness of HCBBO in training MLPs. The accuracy of the results and the convergence of HCBBO are compared to three well-known heuristic algorithms: (a) Biogeography-Based Optimization (BBO), (b) Particle Swarm Optimization (PSO), and (c) Genetic Algorithms (GA). The experimental results show that training MLPs by using HCBBO is better than the other three heuristic learning approaches for big data processing.

A hybrid biogeography-based optimization and fuzzy C-means algorithm for image segmentation

Fuzzy C-means (FCM) clustering method has been widely used in image segmentation that plays an important role in a variety of applications in image processing and computer vision systems, but the performance of FCM heavily relies on the initial cluster centers which are difficult to determine. To solve the problem, the paper proposes a new hybrid method for image segmentation, which first randomly generates a population of initial clustering solutions and then uses an evolutionary algorithm to search for better clustering solutions; at each iteration, the FCM method is applied on each initial clustering solution to produce its segmentation result. Among a set of popular evolutionary algorithms, we find that the biogeography-based optimization (BBO) metaheuristic exhibits good performance on the considered problem. Besides the basic BBO, we have also proposed a set of improved BBO versions in their combination with FCM for image segmentation. Computational experiments on a set of test images show that the proposed method has significant advantage over the basic FCM algorithm and those hybrid algorithms combining FCM with other evolutionary algorithms such as artificial bee colony and particle swarm optimization.

Active disturbance rejection controller design for dynamically positioned vessels based on adaptive hybrid biogeography-based optimization and differential evolution

Vessels with a dynamic positioning system (DPS) are widely applied in ocean resource exploration. Because of the inaccuracy and coupling of the vessel dynamic model, it is important to design a controller that performs well in an oceanic environment. The active disturbance rejection controller (ADRC) is introduced in this study to control the vessel movement and positioning in the DPS. The merit of the ADRC is that it does not need an accurate plant and disturbance model. In the proposed method, an adaptive hybrid biogeography-based optimization (BBO) and differential evolution (DE) are developed. The orthogonal learning (OL) mechanism is employed to achieve adaptive switching to different searching mechanisms between BBO and DE. The proposed adaptive hybrid BBO-DE (AHBBODE) algorithm is then used to optimize the parameters of ADRC; these parameters are not easy to determine by using the trial and error method. Finally, the proposed method is compared with the BBO- and DE-based methods. The results show that better performance is obtained by the proposed method.

Optimizing an equilibrium supply chain network design problem by an improved hybrid biogeography based optimization algorithm

This paper presents a new equilibrium optimization method for supply chain network design (SCND) problem under uncertainty, where the uncertain transportation costs and customer demands are characterized by both probability and possibility distributions. We introduce cost risk level constraint and joint service level constraint in the proposed optimization model. When the random parameters follow normal distributions, we reduce the risk level constraint and the joint service level constraint into their equivalent credibility constraints. Furthermore, we employ a sequence of discrete possibility distributions to approximate continuous possibility distributions. To enhance solution efficiency, we introduce the dominance set and efficient valid inequalities into deterministic mixed-integer programming (MIP) model, and preprocess the valid inequalities to obtain a simplified nonlinear programming model. After that, a hybrid biogeography based optimization (BBO) algorithm incorporating new solution presentation and local search operations is designed to solve the simplified optimization model. Finally, we conduct some numerical experiments via an application example to demonstrate the effectiveness of the designed hybrid BBO.