Based On Biogeography-based Optimization Research Articles

Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization.

Multivariate time-series prediction is a challenging research topic in the field of time-series analysis and modeling, and is continually under research. The echo state network (ESN), a type of efficient recurrent neural network, has been widely used in time-series prediction, but when using ESN, two crucial problems have to be confronted: 1) how to select the optimal subset of input features and 2) how to set the suitable parameters of the model. To solve this problem, the modified biogeography-based optimization ESN (MBBO-ESN) system is proposed for system modeling and multivariate time-series prediction, which can simultaneously achieve feature subset selection and model parameter optimization. The proposed MBBO algorithm is an improved evolutionary algorithm based on biogeography-based optimization (BBO), which utilizes an S -type population migration rate model, a covariance matrix migration strategy, and a Lévy distribution mutation strategy to enhance the rotation invariance and exploration ability. Furthermore, the MBBO algorithm cannot only optimize the key parameters of the ESN model but also uses a hybrid-metric feature selection method to remove the redundancies and distinguish the importance of the input features. Compared with the traditional methods, the proposed MBBO-ESN system can discover the relationship between the input features and the model parameters automatically and make the prediction more accurate. The experimental results on the benchmark and real-world datasets demonstrate that MBBO outperforms the other traditional evolutionary algorithms, and the MBBO-ESN system is more competitive in multivariate time-series prediction than other classic machine-learning models.

Biogeography-Based Optimization for Weight Optimization in Elman Neural Network Compared with Meta-Heuristics Methods

In this paper, we present a learning algorithm for the Elman Recurrent Neural Network (ERNN) based on Biogeography-Based Optimization (BBO). The proposed algorithm computes the weights, initials inputs of the context units and self-feedback coefficient of the Elman network. The method applied for four benchmark problems: Mackey Glass and Lorentz equations, which produce chaotic time series, and to real life classification; iris and Breast Cancer datasets. Numerical experimental results show improvement of the performance of the proposed algorithm in terms of accuracy and MSE eror over many heuristic algorithms.

A modified biogeography-based optimization algorithm with guided bed selection mechanism for patient admission scheduling problems

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.

Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis

Independent Component Analysis (ICA), a type of typical data-driven fault detection techniques, has been widely applied for monitoring industrial processes. FastICA is a classical algorithm of ICA, which extracts independent components by using the Newton iteration method. However, the choice of the initial iterative point of Newton iteration method is difficult; sometimes, selection of different initial iterative points tends to show completely different effects for fault detection. So far, there is still no good strategy to get an ideal initial iterative point for ICA. To solve this problem, a modified ICA algorithm based on biogeography-based optimization (BBO) called BBO-ICA is proposed for the purpose of multivariate statistical process monitoring. The Newton iteration method is replaced with BBO here for extracting independent components. BBO is a novel and effective optimization method to search extremes or maximums. Comparing with the traditional intelligent optimization algorithm of particle swarm optimization (PSO) and so on, BBO behaves with stronger capability and accuracy of searching for solution space. Moreover, numerical simulations are finished with the platform of DAMADICS. Results demonstrate the practicability and effectiveness of BBO-ICA. The proposed BBO-ICA shows better performance of process monitoring than FastICA and PSO-ICA for DAMADICS.

Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization

The computational discovery of DNA motifs is one of the most important problems in molecular biology and computational biology, and it has not yet been resolved in an efficient manner. With previous research, we have solved the single-objective motif discovery problem (MDP) based on biogeography-based optimization (BBO) and gained excellent results. In this study, we apply multi-objective biogeography-based optimization algorithm to the multi-objective motif discovery problem, which refers to discovery of novel transcription factor binding sites in DNA sequences. For this, we propose an improved multi-objective hybridization of adaptive Biogeography-Based Optimization with differential evolution (DE) approach, namely MHABBO, to predict motifs from DNA sequences. In the MHABBO algorithm, the fitness function based on distribution information among the habitat individuals and the Pareto dominance relation are redefined. Based on the relationship between the cost of fitness function and average cost in each generation, the MHABBO algorithm adaptively changes the migration probability and mutation probability. Additionally, the mutation procedure that combines with the DE algorithm is modified. And the migration operators based on the number of iterations are improved to meet motif discovery requirements. Furthermore, the immigration and emigration rates based on a cosine curve are modified. It can therefore generate promising candidate solutions. Statistical comparisons with DEPT and MOGAMOD approaches on three commonly used datasets are provided, which demonstrate the validity and effectiveness of the MHABBO algorithm. Compared with some typical existing approaches, the MHABBO algorithm performs better in terms of the quality of the final solutions.

Power system harmonic estimation using biogeography hybridized recursive least square algorithm

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.

A Comprehensive Study on Specifying an Intelligent Approach to Solve Network Reconfiguration Problem

<p>This paper presents an approach based on biogeography-based optimization (BBO) algorithm to solve the distribution network reconfiguration (DNR) problem for minimizing active power loss. Also it is demonstrated that with considering the nature of reconfiguration problem, among the intelligent algorithms, BBO approach could result the best performance. One of the remarkable advantages of this study is comparing seven different intelligent algorithms in solving network reconfiguration problem. This comparison, not only includes final fitness in optimization process, but also considers number of function evaluation (NFE). The effectiveness of the BBO method has been tested on two different distribution systems and the obtained simulation results are compared with Genetic algorithm (GA), Particle swarm optimization (PSO), Artificial bee colony (ABC), Gravitational search algorithm (GSA), Technical learning based optimization (TLBO) and Cuckoo algorithm (CA). The comparison results show that BBO approach can be an efficient and promising method for solving DNR problems.</p>

Fruit classification by biogeography‐based optimization and feedforward neural network

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

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

Abstract A major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization (GOBBO) algorithm which is enhanced with Oppositional Based Learning (OBL) techniques. We apply both the original BBO and the new Generalized Oppositional BBO (GOBBO) to the PTS problem. The GOBBO-PTS method is compared with other PTS schemes for PAPR reduction found in the literature. The simulation results show that GOBBO and BBO are in general highly efficient in producing significant PAPR reduction and reducing the PTS search complexity.

A New DG Allocation Approach Based on Biogeography-Based Optimization with Considering Fuzzy Load Uncertainty

A new distributed generation placement method based on biogeography-based optimization (BBO) is investigated in this paper. A significant novelty of this study lies in considering fuzzy load uncertainty. For this purpose a fuzzy backward- forward sweep load flow is proposed. The main objectives of this study is minimizing power losses and improving voltage profile. A comparative study between optimal location and sizing under typical load condition and fuzzy load uncertainty is presented. To verify the efficiency of proposed BBO method, it is conducted on IEEE 33 bus distribution system, also a comparative study between proposed BBO approach and particle swarm optimization (PSO), Technical-learning based optimization (TLBO), Artificial bee colony (ABC), Imperialist competitive algorithm (ICA) is investigated. The simulation results show the excellent and superior performance of proposed BBO approach in comparison with the other intelligent methods.

A Novel Method Based on Biogeography-Based Optimization for DG Planning in Distribution System

This paper proposed a novel technique based on biogeography-based optimization (BBO) algorithm in order to optimal placement and sizing of distinct types of Distributed Generation (DG) units in the distribution networks which is applied to improve voltage profile as the main factor for power quality improvement and reduce power losses. In order to promote the investigation to be capable in practical terms, the loads are linearly varied in small steps of 1% from 50% to 150% of the base value. The optimal size and location of distinct types of DGs are found out in each load step. This will aid the distribution network operators (DNOs) to have a long term scheduling for the optimal management of DG units and achieve the maximum performance. To verify the efficiency of proposed method, it has been conducted to IEEE 33-bus radial distribution system. Also, simulation results are compared with the analytical approach and HPSO algorithm (mixed binary and typical particle swarm optimization algorithm). The obtained simulation results demonstrate the better performance and effectiveness of the proposed method. DOI: http://dx.doi.org/10.11591/telkomnika.v15i1.8083

Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm

Cost minimization of Shell-and-tube heat exchangers is a key objective. Traditional design approaches besides being time consuming, do not guarantee the reach of an economically optimal solution. So, in this research, a new shell and tube heat exchanger optimization design approach is developed based on biogeography-based optimization (BBO) algorithm. The BBO algorithm has some good features in reaching to the global minimum in comparison to other evolutionary algorithms. In this study BBO technique has been applied to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping of shell and tube heat exchanger by varying various design variables such as tube length, tube outer diameter, pitch size, baffle spacing, etc. Based on proposed method, a full computer code was developed for optimal design of shell and tube heat exchangers and three different test cases are solved by it to demonstrate the effectiveness and accuracy of the proposed algorithm. Finally the results are compared to those obtained by literature approaches up to the present. The obtained results indicate that the BBO algorithm can be successfully applied for optimal design of shell and tube heat exchangers.