Binary Cuckoo Search Algorithm Research Articles

A hybrid variable selection method combining Fisher's linear discriminant combined population analysis and an improved binary cuckoo search algorithm.

In this paper, a novel hybrid variable selection method for model building by near-infrared (NIR) spectroscopy is proposed for composition measurement in industrial processes. A double-layer structure is designed for variable selection by combining Fisher's linear discriminant combined population analysis (FCPA) and an improved binary cuckoo search algorithm (IBCS). The Fisher classifier combined with model population analysis is used to select the variable interval wherein the useful variables are roughly located even when strong multicollinearity exists among spectral variables. Opposition-based learning (OBL) and jumping genes (JG) are introduced to improve the binary cuckoo search algorithm for the fine selection of key variables, thus avoiding the loss of excellent solutions due to randomness and the local optimum. Different variable selection methods were used to select variables for beer, corn, and diesel fuel datasets, and the partial least squares (PLS) algorithms were used to build calibration models to predict the original extract concentration of beer, the protein and starch content of corn, and the boiling point of diesel fuel, respectively. The results showed that the proposed PLS modeling method based on FCPA-IBCS has higher fitting accuracy and smaller prediction errors.

MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics

MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics

Corrections to “MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics”

Corrections to “MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics”

ODM-BCSA: An Offloading Decision-Making Framework based on Binary Cuckoo Search Algorithm for Mobile Edge Computing

Computational task offloading facilitates real-time applications on constrained mobile devices that require a large amount of processing resources, high storage capacity and battery power. Mobile Edge Computing (MEC) is a computing paradigm that shifts computing resources closer to the user at the network’s edges. Heavy tasks are then offloaded to edge nodes, thereby reducing the computations required on the mobile side. However, offloading computational tasks may result in additional energy consumption and delays, due to network congestion and time spent in server queues. Thus, to minimize completion time and energy consumption, it is essential to optimize offloading decisions, while also considering the financial costs. In this paper, we propose an Offloading Decision-Making Framework based on the Binary Cuckoo Search Algorithm for Mobile Edge Computing (ODM-BCSA). We formulated an offloading problem as a mixed-integer optimization problem to minimize time, energy, and payment costs. We resolved the problem of resource allocation using the Binary Cuckoo Search Algorithm (BCSA). The simulation results revealed that offloading decisions depend on multiple parameters, including the number of mobile devices being handled by the edge server, the bandwidth, and the number of tasks. Decisions made also depended on the priority assigned to each objective. Finally, we compared the ODM-BCSA against a brute force search, proving that the ODM-BCSA is more efficient and greatly minimizes execution time when a huge number of mobile devices are involved (by 99.9 %).

A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

An Application of Binary Cuckoo Search Algorithm to Orienteering Problem

This research applies the cuckoo search metaheuristics model to find solutions to the Orienteering Problem (OP). The OP formulation is useful to model a situation in which someone wants to determine an optimal city route that is subject to a specified time constraint. OP can be categorized into NP Hard Problem which takes a very long time to analytically find the optimal solution as the number of entities involved increases. Therefore, metaheuristics often become an option to deal with this situation. A cuckoo search model based algorithm is developed in this research. An adjustment for discrete combinatorial problem is performed by adopting an idea of binary cuckoo search method. In addition, three types of local search methods are considered to improve the searching performance. This algorithm can eventually find better solutions for some of the 18 cases than two other benchmarked algorithms. Furthermore, experiment on model parameters shows that the worse nest fraction (P_alpha) affects the quality of solutions obtained.

Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification.

For the low optimization accuracy of the cuckoo search algorithm, a new search algorithm, the Elite Hybrid Binary Cuckoo Search (EHBCS) algorithm, is improved by feature weighting and elite strategy. The EHBCS algorithm has been designed for feature selection on a series of binary classification datasets, including low-dimensional and high-dimensional samples by SVM classifier. The experimental results show that the EHBCS algorithm achieves better classification performances compared with binary genetic algorithm and binary particle swarm optimization algorithm. Besides, we explain its superiority in terms of standard deviation, sensitivity, specificity, precision, and F-measure.

Hybrid mechanism for medical data classification

In recent years, medical data mining is the hot topic of research. Earlier extraction was done manually so the process was time consuming. Thus, to solve this drawback machine learning technique was implemented to extract knowledge from the large dataset. In this paper, the medical data processed for removal of missing values, then orthogonal local preserving projection (OLPP) algorithm is employed for feature dimension reduction. Then, fuzzy rules are generated and optimised by binary cuckoo search algorithm. Further, the classification is carried out using the generated fuzzy rules and the best rule is selected based on the accuracy using the decision tree classifier. The performance of the proposed technique has been evaluated using University of California Irvine (UCI) datasets namely Cleveland, Hungarian, Mammographic masses, PID and Switzerland. After observation and comparison, the method achieves highest accuracy of 97.3% for PID dataset whereas the existing method provides 89.5% accuracy for PID dataset.

Optimization of electric distribution network configuration for power loss reduction based on enhanced binary cuckoo search algorithm

This paper presents a new network reconfiguration (NR) method using enhanced binary cuckoo search algorithm (EBCSA) to reduce power loss in the distribution system. Enhanced binary cuckoo search algorithm (EBCSA) is developed by transferring the continuous cuckoo search algorithm (CSA) to the binary domain and adding a new local search mechanism. Calculation results on different test systems demonstrate that EBCSA has capability for searching the optimal network configuration with greater success rate, better optimal solution quality and smaller number of average convergence iterations than those of binary CSA, binary coyote optimization algorithm, binary genetic algorithm and binary particle swarm optimization. Furthermore, the effect of the transfer function transferring from continuous domain to binary domain and the value of the parameters of EBCSA for the NR problem are also evaluated to choose the suitable values. The results show that EBCSA is a tool worth considering for the NR problem.

Classification of Complex Diseases using an Improved Binary Cuckoo Search and Conditional Mutual Information Maximization

With the advancement of various computational techniques, there is an exponential growth in genomic data. To analyze such huge amount of data, there is necessity of efficient machine learning techniques. The genomic data usually suffers from “curse of dimensionality” problem, having large number of n (features) and small number of p (samples), which makes classification task very complex. In the present study, a new intelligent hybrid method based on CMIM (condition mutual information maximization) and novel IBCS (Improved binary cuckoo search) is used for classifying various complex diseases. The CMIM is used to deal with dimensionality problem and IBCS is to select most informative features. Generally the standard BCS (binary cuckoo search algorithm) is used for feature selection but it has problems like low optimization accuracy and low localized searching. The IBCS overcome the shortcomings of BCS, and improved the classification accuracy by choosing best informative feature subset. The proposed technique applied on five different SNPs dataset which are publically available on NCBI GEO. The proposed model attains high classification accuracy and outperformed other feature selection techniques. The IBCS was also compared with other metaheuristics algorithms such as Binary GA, Binary PSO and Binary ACO, and the result shows that it has better classification accuracy.

An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection.

Finding an optimal set of discriminative features is still a crucial but challenging task in biomedical science. The complexity of the task is intensified when any of the two scenarios arise: a highly dimensioned dataset and a small sample-sized dataset. The first scenario poses a big challenge to existing machine learning approaches since the search space for identifying the most relevant feature subset is so diverse to be explored quickly while utilizing minimal computational resources. On the other hand, the second aspect poses a challenge of too few samples to learn from. Though many hybrid metaheuristic approaches (i.e., combining multiple search algorithms) have been proposed in the literature to address these challenges with very attractive performance compared to their counterpart standard standalone metaheuristics, more superior hybrid approaches can be achieved if the individual metaheuristics within the proposed hybrid algorithms are improved prior to the hybridization. Motivated by this, we propose a new hybrid Excited- (E-) Adaptive Cuckoo Search- (ACS-) Intensification Dedicated Grey Wolf Optimization (IDGWO), i.e., EACSIDGWO. EACSIDGWO is an algorithm where the step size of ACS and the nonlinear control strategy of parameter of the IDGWO are innovatively made adaptive via the concept of the complete voltage and current responses of a direct current (DC) excited resistor-capacitor (RC) circuit. Since the population has a higher diversity at early stages of the proposed EACSIDGWO algorithm, both the ACS and IDGWO are jointly involved in local exploitation. On the other hand, to enhance mature convergence at latter stages of the proposed algorithm, the role of ACS is switched to global exploration while the IDGWO is still left conducting the local exploitation. To prove that the proposed algorithm is superior in providing a good learning from fewer instances and an optimal feature selection from information-rich biomedical data, all these while maintaining a high classification accuracy of the data, the EACSIDGWO is employed to solve the feature selection problem. The EACSIDGWO as a feature selector is tested on six standard biomedical datasets from the University of California at Irvine (UCI) repository. The experimental results are compared with the state-of-the-art feature selection techniques, including binary ant-colony optimization (BACO), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), and extended binary cuckoo search algorithm (EBCSA). These results reveal that the EACSIDGWO has comprehensive superiority in tackling the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. Furthermore, the superiority of the proposed algorithm is proved via various numerical techniques like ranking methods and statistical analysis.

Effective Classification of Lung Cancer Using Hybrid Neuro Fuzzy Binary Cuckoo Search Algorithm

Effective Classification of Lung Cancer Using Hybrid Neuro Fuzzy Binary Cuckoo Search Algorithm

A Binary Cuckoo Search for Combinatorial Optimization in Multiyear Pavement Maintenance Programs

For the optimization analysis of pavement maintenance programs, combinatorial optimization is a pervasive problem. Genetic algorithms (GAs) are widely used to solve combinatorial optimization problems in pavement maintenance programs. However, owing to the stochastic search mechanisms underlying GAs, they are more likely to produce a relatively unsatisfactory solution due to premature convergence. Hence, a binary cuckoo search (BCS) algorithm was implemented to solve the optimization problem. To the best of our knowledge, this is the first time that a BCS algorithm has been applied to pavement maintenance management system. Three hypothetical cases are used to investigate and demonstrate the effectiveness of the BCS algorithm, in which uncertainty‐based performance degradation is considered. The results of a comparison between GA and BCS clearly justify the advantages of the search paths underlying the BCS in alleviating premature convergence. Therefore, the BCS algorithm can help decision makers to make more appropriate trade‐off decisions for pavement maintenance programs.

An efficient binary whale optimisation algorithm with optimum path forest for feature selection

Feature selection is an essential process which aims to find the most representative features for image processing and computer vision applications where utilising selected features reduces required time for classification and increases the classification rate. In this study, a new binary whale optimisation algorithm for feature selection is proposed. This optimisation algorithm is based on whales' behaviour. The Optimum-Path Forest (OPF) technique is used as an objective function. This function is much faster than the other classification techniques. The proposed binary whale optimisation algorithm is evaluated using five datasets of colour images. The proposed algorithm outperformed existing optimisation algorithms such as Particle Swarm Optimisation Algorithm (PSOA), Firefly Algorithm (FFA), Gravitational Search Algorithm (GSA), Binary Harmony Search (BHS), Binary Clonal Flower Pollination Algorithm (BCFA), Binary Cuckoo Search Algorithm (BCSA), and Binary Bat Algorithm (BBA) in terms of classification accuracy, number of selected features and execution times.

An Improved Binary Cuckoo Search Algorithm for Solving Unit Commitment Problems: Methodological Description

The unit commitment problem is a large-scale, nonlinear, and mixed-integer optimization problem in an electric power system. Numerous researchers concentrate on minimizing its total generation cost. Cuckoo search is an efficient metaheuristic swarm-based approach that balances between local and global search strategy. Owing to its easy implementation and rapid convergence, it has been successfully used to solve a wide variety of optimization problems. This paper proposes an improved binary cuckoo search algorithm (IBCS) for solving the unit commitment problem. A new binary updating mechanism is introduced to help the IBCS choose a right search direction, and a heuristic search method based on a novel priority list can prevent it from being trapped into local optima. A 4-unit system is used as an example to validate the effectiveness of the proposed method.

Multi-objective binary cuckoo search for constrained project portfolio selection under uncertainty

One of the recurrent complex decisions faced by organisations is project portfolio selection (PPS) in which a group of the most beneficial projects must be selected from a set of candidate projects. Accordingly, effective means for selecting projects must be employed in order for the organisation to survive in today's extremely competitive business environments. This paper proposes a framework that integrates a multi-objective binary cuckoo search (MOCS) algorithm with Monte Carlo simulation to help decision makers select the best portfolio based on a given set of parameters (criteria and performance values) and several characteristics (uncertainty, constraints and multi-objective). Performance of the proposed framework was measured against a published research on two cases: 1) when transforming all objectives into one, the proposed algorithm outperformed recent algorithms used to solve the problem on a large-scale where these algorithms fail to reach an optimal solution; 2) when dealing with a multi-objective case, the proposed algorithm outperformed reported earlier results with 90% of the non-dominated solutions were obtained by it. [Received 30 September 2016; Revised 29 January 2017; Revised 24 May 2017; Revised 11 August 2017; Accepted 25 August 2017]

Multi-Objective Binary Cuckoo Search for Constrained Project Portfolio Selection Under Uncertainty

One of the recurrent complex decisions faced by organisations is project portfolio selection (PPS) in which a group of the most beneficial projects must be selected from a set of candidate projects. Accordingly, effective means for selecting projects must be employed in order for the organisation to survive in today's extremely competitive business environments. This paper proposes a framework that integrates a multi-objective binary cuckoo search (MOCS) algorithm with Monte Carlo simulation to help decision makers select the best portfolio based on a given set of parameters (criteria and performance values) and several characteristics (uncertainty, constraints and multi-objective). Performance of the proposed framework was measured against a published research on two cases: 1) when transforming all objectives into one, the proposed algorithm outperformed recent algorithms used to solve the problem on a large-scale where these algorithms fail to reach an optimal solution; 2) when dealing with a multi-objective case, the proposed algorithm outperformed reported earlier results with 90% of the non-dominated solutions were obtained by it. [Received 30 September 2016; Revised 29 January 2017; Revised 24 May 2017; Revised 11 August 2017; Accepted 25 August 2017]

A complex-valued encoding wind driven optimization for the 0-1 knapsack problem

This paper presents a complex-valued encoding wind driven optimization (CWDO) with a greedy strategy for the 0-1 knapsack problem. We introduce a complex-value encoding method, which can be viewed as an effective global optimization strategy, and a greedy strategy, which can be viewed as an enhanced local search strategy into wind driven optimization. These strategies increase the diversity of the population and avoid premature convergence. This paper presents three types of test cases for evaluation: standard, small-scale, and large-scale test cases. The experimental results show that the proposed algorithm is suitable for these three cases. Compared to the complex valued cuckoo search algorithm, greedy genetic algorithm, wind driven optimization, binary cuckoo search algorithm, bat algorithm and particle swarm optimization, the performance, stability, and robustness of the CWDO algorithm is better. The simulation results show that the CWDO algorithm is an effective and feasible method for solving the 0-1 knapsack problem.

A Hybrid Binary Cuckoo Search and Genetic Algorithm for Feature Selection in Type-2 Diabetes

Data mining techniques are applied in bioinformatics to analyze biomedical data. When the number of features related to the data is irrelevant, the classifiers will produce unsatisfactory results. This paper addresses the need to analyze the data for extracting relevant features. A number of feature selection algorithms are developed in the field of medical data to address feature selection. In this paper, an intelligent hybrid optimal feature selection algorithm is proposed for Type-2 diabetes with improved classification accuracy. This work proposes an intelligent Hybrid Binary Cuckoo Search (CS) and Genetic Algorithm (GA) for selecting the important features of Type-2 diabetes. In HBCS-GA, exploration and exploitation of CS is improved using genetic operators to select relevant features with better accuracy. To validate the model, a 10-fold cross-validation strategy is used. The proposed algorithm produces 99.31% accuracy to diagnose the disease. The performance of HBCS-GAis also compared with other approaches. Also, the model validation with reduced features is performed with Decision Tree(DT), Bayesian Network(BN), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. The accuracies obtained are 94.46%, 96.07%, 98.84%, 96.79% respectively. The results also showed that HBCS-GA achieved high classification accuracy than the other approaches. Keywords: Diabetes, normalization, classification, instance selection, wrapper feature selection, cuckoo search, genetic algorithm.

A binary clonal flower pollination algorithm for feature selection

Feature selection problem has been detected essentially in the last years. It is a step that is considered the prerequisite of the classification step. For the feature selection problem, the goal is to find out the most important subset of features that represent the original features in a certain domain. The selected features are used in optimization of a certain fitness function, so the feature selection problem can be seen as an optimization problem. This paper presents a new hybrid algorithm that combines Clonal Selection Algorithm (CSA) with Flower Pollination Algorithm (FPA) to compose Binary Clonal Flower Pollination Algorithm (BCFA) to solve the feature selection problem. The accuracy of the Optimum-Path Forest (OPF) classifier is used as an objective function. The experiments were implemented on three public datasets and demonstrated that the proposed hybrid algorithm achieved remarkable results in comparison with other well-known algorithms such as Binary Cuckoo Search Algorithm (BCSA), Binary Bat Algorithm (BBA), Binary Differential Evolution Algorithm (BDEA) and Binary Flower Pollination Algorithm (BFPA).