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Particle Swarm Optimization algorithm (PSO) has been widely utilized for addressing optimization problems due to its straightforward implementation and efficiency in tackling various test functions and engineering optimization problems. Nevertheless, PSO encounters issues like premature convergence and a lack of diversity, particularly when confronted with complex high-dimensional optimization tasks. In this study, we propose an enhanced version of the Island Model Particle Swarm Optimization (IMPSO), where island models are integrated into the PSO algorithm based on several migration strategies. The first contribution consists in applying a new selection and replacement strategies based on tabu search technique, while the second contribution consists in proposing a dynamic migration rate relying on the Biogeography-Based Optimization technique. To assess and validate the effectiveness of the proposed method, several unconstrained benchmark functions are applied. The obtained results confirm that the approach yield better performance than the old version of IMPSO for solving NP-hard optimization problems. Compared to the performance of other well-known evolutionary algorithms, the proposed approach is more efficient and effective. [ABSTRACT FROM AUTHOR]

In this paper, a novel attempt is made to incorporate the two effective algorithm strategies, where BBO has a strong exploration and Salp Swarm Algorithm (SSA) is used for exploitation of the search space. The proposed algorithm is tested on IEEE CEC 2014 and statistical, convergence graphs are given. The proposed algorithm is also applied to 10 real life problems and compared with its counterpart algorithm. Results obtained by above experiments have demonstrated the outperformance of the hybrid version of BBO over other algorithms.

In this paper, a novel attempt is made to incorporate the two effective algorithm strategies, where BBO has a strong exploration and Salp Swarm Algorithm (SSA) is used for exploitation of the search space. The proposed algorithm is tested on IEEE CEC 2014 and statistical, convergence graphs are given. The proposed algorithm is also applied to 10 real life problems and compared with its counterpart algorithm. Results obtained by above experiments have demonstrated the outperformance of the hybrid version of BBO over other algorithms. [ABSTRACT FROM AUTHOR]

Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users' private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the BBODLPEDC model is to distinguish emails between legitimate and phishing. The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning, tokenization, and stop word elimination. Besides, TF-IDF model is applied for the extraction of useful feature vectors. Moreover, optimal deep belief network (DBN) model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process. The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions. Extensive comparative studies reported the superior outcomes of the BBODLPEDC model over the recent approaches. [ABSTRACT FROM AUTHOR]

One of the complex combinatorial optimization problems is the Patient admission scheduling problem (PASP), which is concerned with assigning the patients arriving into a hospital to available beds to get medical services. The objective of PASP is to maximize the patients comfort, medical treatment effectiveness, and hospital utilization. This research proposes a new approach based on Biogeography-Based Optimization (BBO) algorithm for tacking the PASP. BBO was inspired from the idea of species migration between different habitats. Due to the complexity of the search space in PASP, the original BBO has been equipped with a guided bed selection (GBS) mechanism in order to improve its results and performance, as well as the operator capabilities of BBO which are modified to improve its diversity. These three variants of BBO are compared with each other using six de facto data sets that are widely used in the literature with varying sizes and complexity. The modified BBO is able to yield better results than the other variants. In a nutshell, this paper provides a new PASP method that can be considered an efficient alternative for the scheduling domain to be used by other researchers.

This paper will investigate the efficiency of an optimal Fuzzy Logic Controller (FLC) for a decentralized source which is established on the basis of a Solid Oxide Fuel Cell (SOFC) that is linked to the electrical network through a voltage inverter and a boost converter. To serve the purpose of this research, a Biogeography Based Optimization (BBO) is applied in order to adjust the parameters of the membership functions (the centers and the widths of the gaussian membership functions in inputs and output) for the purpose of improving the efficacy of traditional fuzzy logic controller. The given control methods have proved to be effective drawing from simulation results, and show that fuzzy logic controller tuned by biogeography based optimization is better and more robust than the traditional fuzzy logic controller for decentralized source based on a SOFC. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms are one of the most emerging fields in pattern recognition and many computationally complex problems can be solved using evolutionary algorithms as heuristics. Face recognition is one of the most studied research topics all due to its very vast application in almost every field of the present hour but there is no such algorithm present which almost give 100% accuracy on different datasets. A variant of Biogeography Based Optimization (BBO) which includes the features of evolution, extinction and changes in the methodology of migration (immigration and emigration) have been applied to solve the problem of face recognition. Evolutionary algorithms iteratively try to improve the candidate solutions for a given fitness function and BBO is one such algorithm. This inclusion of extra features improves the performance of BBO significantly. The most important task in face recognition is feature extraction which helps to differentiate between the faces. A combination of PCA (Principal Component Analysis) for feature extraction and SVM (Support Vector Machine) for classification. The proposed variant of BBO is applied with the vectors as the candidate solution and an optimal set of eigenfaces are obtained which are then used to project the points to a new feature space where the interclass distance is minimal at the same time intraclass distance is maximal. The test images are then classified in this feature space. On testing our algorithm for 5 face different datasets namely Extended Yale B, MUCT, Faces96, Georgia Tech and Grimace the accuracy obtained with such a large variety of datasets clearly shows the effectiveness of our proposed algorithm. Even though the datasets are varied in terms of size, length of images, brightness, ethnicity of subjects, expressions, focus on the facial parts the algorithm achieve 100% accuracy on Grimace and Extended Yale B and a near to 100% accuracy of 99% on MUCT and 99.50% on faces96. On Georgia Tech dataset, an accuracy of 97.34% has been achieved which is enough to prove a significant improvement from the previous used algorithms of face detection. [ABSTRACT FROM AUTHOR]

One of the complex combinatorial optimization problems is the Patient admission scheduling problem (PASP), which is concerned with assigning the patients arriving into a hospital to available beds to get medical services. The objective of PASP is to maximize the patients comfort, medical treatment effectiveness, and hospital utilization. This research proposes a new approach based on Biogeography-Based Optimization (BBO) algorithm for tacking the PASP. BBO was inspired from the idea of species migration between different habitats. Due to the complexity of the search space in PASP, the original BBO has been equipped with a guided bed selection (GBS) mechanism in order to improve its results and performance, as well as the operator capabilities of BBO which are modified to improve its diversity. These three variants of BBO are compared with each other using six de facto data sets that are widely used in the literature with varying sizes and complexity. The modified BBO is able to yield better results than the other variants. In a nutshell, this paper provides a new PASP method that can be considered an efficient alternative for the scheduling domain to be used by other researchers. [ABSTRACT FROM AUTHOR]

The present study aims to provide a suitable approach to optimize transmission line towers with size, shape and panel design variables. MSTOWER software was used for modeling, analysis and design of the transmission tower. The design requirements applied to the structure were in accordance with ASCE10-97 standard. Transmission line towers are optimized in two ways. The first method combines Biogeography based Optimization (BBO) algorithm with MSTOWER software called BBO-MSTOWER and the second method, called BBO-ANFIS, uses an adaptive fuzzy neural inference system (ANFIS). To analyze the structure in the optimization process with the aim of reducing the computations and time of the optimization process. In order to evaluate the proposed method, two full-scale transmission towers were optimized as numerical models using the above two methods. Finally, the optimal design and time required in the two optimization methods were compared with each other as well as with the initial design of the tower and the results reported in previous studies. The results showed that with acceptable accuracy, ANFIS resulted in a significant reduction in the calculation time of the entire optimization process. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal layout of wind turbines (WTs) within a wind farm. Finding the best placement of WTs in a wind farm is a challenging process due to the existence of multiple wake effects. A biogeography based optimization (BBO) algorithm method is proposed to search for the optimal location of WTs in a wind farm (WF), to maximize the power produced by the WF and improve the annual economic performance of the WF. A wind turbine (WT) that operates in the wake of one or more other turbines is subject to lower flow and therefore produces less power. When designing a wind project, the arrangement of the turbines with each other in a wind farm is a very important factor. The best layout of a wind farm is to achieve the optimal placement of the turbines in relation to each other in a given area to maximize the efficiency of the whole wind farm and reduce its cost. A dense configuration would result in considerable power losses. Each turbine must have a sufficient distance from other turbines in the WF where the optimal number of turbines should be placed. The BBO approach is conducted on a 2 km x 2 km wind farm assuming a constant wind speed of 12 m/s with a fixed wind direction, for solving the wind farm layout optimization problem in two different configurations which include 26 and 30 WTs respectively. A comparison of the results obtained with the previous studies shows that the BBO is more efficient in terms of maximizing power output and economic profitability of the same wind farm model, which validates that BBO performs effectively in optimizing WTs placement within WF. BOO provides the greatest improvement in the optimal layout, for example, in the case of the layout for 30 WTs. The power output reaches 15,383 KW, the agreement between the ideal and the optimal layout is more favorable. The difference in output power is only 169 KW (1%). Knowing that the ideal layout is reached if all WTs receive a wind flow with a maximum wind speed of 12 m/s. Furthermore, a case study of wind turbine layout optimization using the BBO program on the Alta X wind farm has been performed under variable wind speed and variable wind direction. The results indicate that the optimized layout of the Alta X wind farm achieves a 12% increase in the power output for a similar cost when compared to the original layout of Alta X. It is also more appropriate for evaluating the wind farm layout in the Wind Power Project (WPP). [ABSTRACT FROM AUTHOR]

In a sensorless control of PMSM based on Extended Kalman Filter (EKF), the correct selection of system and measurement noise covariance has a great influence on the estimation performances of the filter. In fact, it is extremely difficult to find their optimal values by trial and error method. Therefore, the main contribution of this work is to prove the efficiency of Biogeography-Based-Optimization (BBO) technique to obtain the optimal noise covariance matrices Q and R. The BBO and EKF combination gives a BBOEKF algorithm, which allows to estimate all the state variables of PMSM drive particularly, the rotor position and speed. In this paper, three evolutionary algorithms namely Particle Swarm Optimization (PSO), genetic algorithms (GAs) and BBO are used to get the best Q and R of EKF. Simulations tests performed in Matlab Simulink environment show excellent performance of BBO-EKF compared to GAs-EKF and PSOEKF approaches either in resolution or in convergence speed. [ABSTRACT FROM AUTHOR]

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.

Hub networks have always been acriticalissue in locating health facilities. Recently, a study has been investigated by Cocking et al. (2006)in Nouna health district in Burkina Faso, Africa, with a population of approximately 275,000 people living in 290 villages served by 23 health facilities. The travel times of the population to health services become extremely high during the rainy season, since many roads are unusable. In this regard, for many people, travelling to a health facility is a deterrent to seeking proper medical care. Furthermore, in real applications of hub networks, the travel times may vary due to traffic, climate conditions, and land or road type.To handle this challenge this paper considers the travel times are assumed to be characterized by trapezoidal fuzzy variables in order to present a fuzzy green capacitated single allocation p-hub center system (FGCSApHCP) with uncertain information. The proposed FGCSApHCP is redefined into its equivalent parametric integer nonlinear programming problem using credibility constraints. The aim is to determine the location of pcapacitated hubs and the allocation of center nodes to them in order to minimize the maximum travel time in a hub-and-center network in such uncertain environment. As the FGCSApHCP is NP-hard, a novel algorithmcalledoppositionbiogeography based optimizationis developed to solve that. This algorithm utilizes a binary oppositionbased learning mechanism to generate a diversity mechanism. At the end, both the applicability of the proposed approach and the solution methodologies are demonstrated using GAMS/BARON Software under severalkind of problems. Sensitivity analyses on the number of hubs and center nodes are conducted toprovide more insights as well.

A major role is played by the analysis of power system security in heightening system security and in system collapse condition avoidance. This article presents a cutting edge mechanism which is devised applying transmission line loadings as well as variance in bus voltage magnitude. The use of flexible alternating current transmission systems devices improves the objectives of generation fuel charges in addition to the severity index proposed which were investigated considering the contingency circumstances of generator(s) or/and transmission channel(s). To boost system security in spite of contingency circumstances in the existence of unified power flow controller or UPFC, it would be most appropriate to pinpoint a most advantageous position to install aforementioned device. We propose a model of UPFC where power insertion is done by using voltage source. Also a procedure to incorporate the same and a strategy to find optimum position has been proposed which uses line overload sensitivity indices. This work mainly focused on establishment of available transfer capability on the heavily congested line. The proposed congestion management scheme alleviates the heavy stress in transmission line and provides an ample corridor for the power to flow. Biogeography‐based optimization or BBO in short, is a technique which is a growing recognized optimization method which has been lucratively engaged in solving intricate optimization problem in dissimilar fields. The BBO provides better results than the metaheuristic counter parts such as Genetic Algorithm and Particle Swarm Optimization. The effectiveness of proposed BBO has been tested on standard IEEE 30 bus system and the results are compared with classic methods and other metaheuristic methods. This is established through the MATLAB package. Improved bus voltage profile was also attained and it can be inferred from the outcome that the prospective approach can drastically enhance security of power system when comparing with other optimization methods. [ABSTRACT FROM AUTHOR]

Summary: Rapid growth of electric vehicles (EVs) has necessitated the devolvement of sustainable and easily accessible charging stations. Transport sector electrification and increased popularity of EVs make researcher to search for charging stations. In this paper, a new methodology regarding electric vehicle charging spot is proposed. In the study, allocation of the parking lot and capacitor is suggested for congestion management along with reactive power compensation. To this end, sensitivity analysis is performed by evaluating the inverse Jacobian matrix from the power flow studies. In order to optimally determine the size of parking lot, biogeography‐based optimization (BBO) technique is adopted. The effectiveness of the anticipated technique is tested on adapted IEEE 34‐bus distribution network. The outcome attained by BBO technique is equated with particle swarm optimization. [ABSTRACT FROM AUTHOR]

Text classification reduces the time complexity and space complexity by dividing the complete task into the different classes. The main problem with text classification is a vast number of features extracted from the textual data. Pre-processed dataset have many features, some of which are not desirable and act only like noise. In this paper, a novel approach for optimal text classification based on nature-inspired algorithm and ensemble classifier is proposed. In the proposed model, feature selection was performed with Biogeography Based Optimization (BBO) algorithm along with ensemble classifiers (Bagging). The use of ensemble classifiers for classification delivers better performance for optimal text classification as compared to an individual classifier, and hence, improving the accuracy. Ensemble classifiers combines the weakness of individual classifiers. The individual classifiers are unable to improve the classification results when compared to ensemble classifier. The selected features, after feature selection using BBO algorithm, are classified into various classes using six machine learning classifier. The experimental results are computed on ten text classification datasets taken from UCI repository and one real-time dataset of an airlines. The four different measures namely; Accuracy, Precision, Recall and F- measure are used to validate performance of our model with ten-fold cross-validation. For feature selection process, a comparison is performed among state-of-the-art algorithms available in the literature. Results shows that BBO for feature selection outperforms the other similar nature-based optimization techniques. Our proposed approach of BBO with ensemble classifier is also compared with techniques proposed by other researchers and we analyzed the results quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Mobile Ad Hoc Network (MANET) is a wireless network that does not have any fixed structure. MANET is a collection of independent mobile nodes that can communicate with each other through radio waves. In conventional Internet Protocol (IP) forwarding, each router creates forwarding decision on the basis of IP header information. The router has to analyse the packet and routing table at each hop to take the decision for data transmission. Since, inappropriate decisions in data transmission causes delay in packet delivery. This can be overcome with the help of a special feature of Multiprotocol Label Switching (MPLS) called Label switching. MPLS is a switching mechanism that plays a good role in routing, switching and forwarding using small labels. The MPLS is integrated into MANET to improve network performance. In this paper, an effective routing strategy is introduced to reduce the transmission time between routers during the data transmission. Here the Ad Hoc On-Demand Distance Vector (AODV) with Biogeography Based Optimization (BBO) based routing method is used to identify the optimal route among the routers of the MPLS based MANET. The proposed methodology is named as AODV-BBO. Three different objective functions are considered in this routing strategy viz., residual energy, distance and number of hops. Moreover, the rerouting is performed when the node or link failure occurs in the network. This rerouting is used to overcome the packet drop through the MPLS based MANET. The performance of AODV-BBO methodology is analysed in terms of percentage of alive nodes, dead nodes, energy consumption, end to end delay and bandwidth. The performance of the AODV-BBO methodology is compared with two existing methods P2R2 and PS-ROGR. Result showed that, the energy consumption of the AODV-BBO methodology with 400 nodes is 536.52 J, which is less compared to the PS-ROGR method. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid chaotic biogeography-based optimization (HCBBO) for solving the sequence dependent setup times flowshop scheduling problem with the objective of minimizing the total weighted tardiness. First of all, a largest-order-value rule is employed to transform continuous vectors into discrete job permutations. Second, the chaotic theory and the problem-specific Nawaz-Enscore-Ham heuristic are applied to compose the initial population with the property of intensification and diversification. Third, an improved biogeography-based optimization is introduced to improve the global search ability by designing new migration and mutation schemes. Meanwhile, a further local search is proposed and embedded in HCBBO to enhance the quality of the elite habitats. In addition, an effective perturbation is applied to avoid the solutions getting trapped in the local optima. Computation comparison experiments of seven benchmark algorithms on the Taillard benchmark problems are provided to verify the efficiency of the proposed algorithm. From the experiment results, it can conclude that HCBBO beats other compared algorithms effectively with higher quality and robustness solutions.

Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time-consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro-fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA). A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30%) sets. To perform the proposed framework, the step-wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS-BBO and ANFIS-ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), root-mean-square error (RMSE). The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS-BBO and ANFIS-ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS-BBO and ANFIS-ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition, as regards ANFIS-BBO and ANFIS-ICA, a total areas of 39.30% and 35.39% were classified as highly susceptible to flooding. Therefore, the proposed ensemble framework can be used for flood susceptibility mapping in other sites with similar geo-environmental characteristics for taking measures to manage and prevent flood damages. Image 1 • Prediction power of two novel ensemble methods for flood susceptibility mapping. • The proposed ensemble methods can improve the prediction performance of ANFIS. • The proposed methods can accurately produce flood susceptibility maps for mitigation and management. [ABSTRACT FROM AUTHOR]

Electrical discharge machining is a non-conventional machining method that is used for machining of hard-to-machine materials, components in which length to diameter ratio is very high or products with a very complicated shape. The process is commonly used in automobile, biomedical, chemical, aerospace, tool and die industries etc. It is very essential to select optimum values of input process parameters to maximize the machining performance. In this paper an attempt has been made to carry out multiobjective optimization of the surface roughness (SR) and material removal rate (MRR) for the EDM process of EN 19 on a CNC EDM machine using copper electrode through evolutionary optimization techniques like teaching Learning Based Optimization (TLBO) technique and biogeography-based optimization (BBO) technique. The input parameter considered for the optimization are Current (A), Voltage (V), Pulse off time (µs), and Pulse on time (µs). TLBO and BBO technique wereused to obtain maximum MRR and minimize the SR. It was found that SR and MRR increased linearly when discharge current was in mid-range however non-linear increment of MRR and SR was found when the current was too small or too large. Scanning Electron Microscope (SEM) images also indicated a decreased SR. In addition, obtained optimized values were validated for testing the significance of the TLBO and BBO technique and a very small error value of MRR and SR was found. BBO outperformed TLBO in every aspect like less percentage error and better-optimized values, however, TLBO took less computation time than the BBO. [ABSTRACT FROM AUTHOR]

Feature selection is used as a preprocessing step in the resolution of many problems using machine learning. It aims to improve the classification accuracy, speed up the model generation process, reduce the model complexity and reduce the required storage space. Feature selection is an NP-hard combinatorial optimization problem. It is the process of selecting a subset of relevant, non-redundant features from the original ones. Among the works that are proposed to solve this problem, few are dedicated for intrusion detection. This paper presents a new feature selection approach for intrusion detection, using the Biogeography Based Optimization (BBO) algorithm. The approach which is named Guided Adaptive Binary Biogeography Based Optimization (GAB-BBO) uses the evolutionary state estimation (ESE) approach and a new migration and mutation operators. The ESE approach we propose in this paper uses the Hamming distance between the binary solutions to calculate an evolutionary factor f which determines the population diversity. During this process, fuzzy logic is used through a fuzzy classification method, to perform the transition between the numerical f value and four evolutionary states which are : convergence, exploration, exploitation and jumping out. According to the state identified, GAB-BBO adapts the algorithm behavior using a new adaptive strategy. The performances of GAB-BBO are evaluated on benchmark functions and the Kdd’99 intrusion detection dataset. In addition, we use other different datasets for further validation. Comparative study with other algorithms is performed and the results show the effectiveness of the proposed approach.

This paper is dedicated to the optimization of nonlinear controllers basing of an enhanced Biogeography Based Optimization (BBO) approach. Indeed, The BBO is combined to a predator and prey model where several predators are used with introduction of a modified migration operator to increase the diversification along the optimization process so as to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems. Simulations are carried out over a Mass spring damper and an inverted pendulum and has given remarkable results when compared to genetic algorithm and BBO.

In this paper, an efficient biogeography-based optimization (EBBO) method is proposed to synthesize the circular antenna arrays (CAA) for specific radiation beam pattern properties and null controls. The proposed method achieves the desired beam patterns by jointly optimizing the excitation currents as well as the spaces between the array elements. Three improved components, including chaotic search theory, model learning method and a new random perturbation operator, are introduced into the standard biogeography-based optimization (BBO) to improve the performance of the algorithm. Simulation results show that the maximum sidelobe level obtained by EBBO can be suppressed effectively compared with other algorithms. Moreover, the null controlling performance of EBBO is the best among the algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are constrained with limited battery lifetime. Cluster head near the base station act as relays to the cluster heads far from the base station resulting in fast depletion of the cluster head close to the base station. To overcome this problem mobile sink have been used in the past. The proposed algorithm finds the optimum path and sojourn location of the mobile sink using biogeography based optimization (BBO). BBO not only converge faster as compared to GA but it also gives more optimized results. The proposed algorithm is compared with previous protocols such as LEACH, GA based clustering, PSO based clustering. The algorithm is implemented using MATLAB simulation tool. The proposed algorithm performs better both in terms of lifetime of the cluster heads as well as lifetime of the entire network. [ABSTRACT FROM AUTHOR]

Detecting communities in complex networks is one of the most important issues considered when analyzing these kinds of networks. A majority of studies in the field of community detection tend to detect communities through analyzing linkages of the networks. What this paper aims to achieve is to reach to a trade-off between similarity of nodes' attributes and density of connections in finding communities of social networks with node attributes. Since the community detection problem can be modeled as a seriously non-linear discrete optimization problem, we have hereby proposed a multi-objective discrete Biogeography Based Optimization (BBO) algorithm to find communities in social networks with node attributes. This algorithm uses the Pareto-based approach for community detection. Also, we introduced a new metric, SimAtt, to measure the similarity of node attributes in a community of a network and used it along with Modularity, which considers the linkage structure of a network to detect communities, as the two objective functions of the proposed method to be maximized. In the proposed method, a two phase sorting strategy is introduced which uses the non-dominated sorting and Crowding-distance to sort the generated solution of a population in each iteration. Moreover, this paper introduces a method for mutation probability approximation and uses a chaotic mechanism to dynamically tune the mutation probability in each iteration. Additionally, two novel strategies are introduced for mutation in unweighted and weighted networks. Since the final output of the proposed method is a set of non-dominated (Pareto-optimal) solutions, a metric named alpha_SAM is proposed to determine the best compromise solution among these non-dominated ones. Quantitative evaluations based on extensive experiments on 14 real-life data sets reveals that the method presented in this study achieves favorable results which are quite superior to other relevant algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid method based on biogeography-based optimization (BBO) and recursive least square (RLS) algorithms, called BBO–RLS, to solve harmonic estimation problem in case of time varying power signal in presence of different noises. BBO algorithm searches for the global optimum mainly through two steps: migration and mutation. The basic BBO algorithm is combined with RLS in an adaptive way to sequentially update the unknown parameters (weights) of the harmonic signal. Practical validation is also made with the experimentation of the algorithm with real time data obtained from a solar connected inverter system panel with power quality analyzer and estimation is performed under simulation. Comparison of the results achieved with the proposed algorithm demonstrates its superiority over other recently reported five algorithms like GA, PSO, BFO, F-BFO with Least Square (LS), and BFO–RLS in terms of accuracy, convergence and computational time. [ABSTRACT FROM AUTHOR]

This paper uses a new algorithm namely biogeography based optimization (BBO) intended for the simultaneous placement of the distributed generation (DG) units and the capacitor banks in the distribution network. The procedure of optimization has been conducted in the presence of nonlinear loads (a cause of harmonic injection). The purpose of simultaneous optimal placement of the DG and the capacitor is the reduction of active and reactive losses. The difference in the values of loss reduction at different levels of the load have been included in the objective function and the considered objective function includes the constraints of voltage, size and the number of DG units and capacitor banks and the allowable range of the total harmonic distortion (THD) of the total voltage in accordance with the IEEE 519 standards. In this paper the placement has been performed on two load types ie constant and mixed power, moreover the effects of load models on the results and the effects of optimal placement on reduction of the THD levels have also been analyzed. The mentioned cases have been studied on a 33 bus radial distribution system. [ABSTRACT FROM AUTHOR]

This paper presents an extended model of biogeography based optimization (BBO) as opposed to the classical BBO wherein the HSI value of a habitat is not solely dependent upon the emigration and immigration rates of species but the HSI value is a function of different combinations of SIVs depending upon the characteristics of the habitat under consideration. The extended model also introduces a new concept of efforts required in migration from a low HSI solution to a high HSI solution for optimization in BBO. Hence, the proposed extended model of BBO presents an advanced optimization technique that was originally proposed by Dan Simon as BBO in December, 2008. Based on the concepts introduced in our extended model of BBO and its mathematics, we design a two – phase anticipatory system architecture for intelligent preparation of the battlefield which is the targeted optimization problem in our case. The proposed anticipatory system serves a dual purpose by predicting the deployment strategies of enemy troops in the battlefield and also finding the shortest and the best feasible path for attack on the enemy base station. Hence, the proposed anticipatory system can be used to improve the traditional approaches, since they lack the ability to predict the destination and can only find a suitable path to the given destination, leading to coordination problems and target misidentification which can lead to severe casualties. The designed system can be of major use for the commanders in the battlefield who have been using traditional decision making techniques of limited accuracy for predicting the destination. Using the above natural computation technique can help in enabling the commanders in the battlefield for intelligent preparation of the battlefield by automating the process of assessing the likely base stations of the enemy and the ways in which these can be attacked, given the environment and the terrain considerations. The results on two natural terrain scenarios that of plain/desert region of Alwar and hilly region of Mussourie are taken to demonstrate the performance of the technique where the proposed technique clearly outperforms the traditional methods and the other EAs like ACO, PSO, SGA, SOFM, FI, GA, etc. that have been used till date for path planning applications on satellite images with the smallest pixel count of 351 and 310 respectively. For location prediction application, the highest prediction efficiencies of the traditional method on Alwar and Mussourie was only 13% and 8% respectively as compared to the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new enhanced biogeography-based optimization algorithm (EBBO) for a complex variant of vehicle routing problem (VRP) called HVRPMBTW (vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows). This variant is characterized by a limited number of vehicles with various capacities and costs. The vehicles serves two types of customers: linehaul customers and backhaul customers. Each customer must be visited in a specific interval of time. We propose to improve and to adapt the biogeography-based optimization (BBO) approach to the problem by integrating simulated annealing algorithm to enhance solution quality, at each iteration. This new approach was tested on benchmarks and produces very satisfactory results compared to other approaches, PSO and ACO []. [ABSTRACT FROM AUTHOR]

Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. [ABSTRACT FROM AUTHOR]

Biogeography-based optimization (BBO) is a new evolutionary algorithm which mimics the immigration and emigration of species among islands. Used widely in packaging and printing to obtain a colorful appearance, the spot color matching (SCM) is formulated as a complex multi-dimensional optimization problem. In this article, BBO is combined with the harmony search (HS) and opposition-based learning (OBL) approaches to construct an effective hybrid algorithm for solving the SCM problem. HS is used to enhance the local searching ability of BBO, and OBL is employed to increase the diversity of initial population; consequently, the exploration and exploitation abilities of the hybrid algorithm are enhanced and well balanced. Experiment results are presented to show the effectiveness of the proposed scheme. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 607-615, 2014 [ABSTRACT FROM AUTHOR]