Binary Cuckoo Search Research Articles

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).

A Novel Hybrid Cuckoo Search Algorithm with Global Harmony Search for 0–1 Knapsack Problems

AbstractCuckoo search (CS) is a novel biologically inspired algorithm and has been widely applied to many fields. Although some binary-coded CS variants are developed to solve 0–1 knapsack problems, the search accuracy and the convergence speed are still needed to further improve. According to the analysis of the shortcomings of the standard CS and the advantage of the global harmony search (GHS), a novel hybrid meta-heuristic optimization approach, called cuckoo search Algorithm with global harmony search (CSGHS), is proposed in this paper to solve 0–1 knapsack problems (KP) more effectively. In CSGHS, it is the combination of the exploration of GHS and the exploitation of CS that makes the CSGHS efficient and effective. The experiments conducted demonstrate that the CSGHS generally outperformed the binary cuckoo search, the binary shuffled frog-leaping algorithm and the binary differential evolution in accordance with the search accuracy and convergence speed. Therefore, the proposed hybrid algorithm is...

Optimal feature subset selection in high dimensional data clustering

Feature subset selection is the process of identifying and removing many irrelevant and redundant features. Initially, the input micro array dataset is selected from the medical database. Then, preprocessing step is done in the input dataset. The resultant output is fed to the second step; here, the features are optimally selected using clustering and tree generation process. In our proposed technique, the modified kernel-based fuzzy c-means clustering algorithm with optimal minimum spanning tree algorithm is applied on the high dimensional dataset to select the important features, in which the optimal features are selected by means of binary cuckoo search. Next, the classification is done through neuro fuzzy classifier. At last, the experimentation is performed by means of different micro array dataset. The result proves that neuro fuzzy classifier outperformed the existing approach by attaining maximum accuracy of 89% for GLA-BRA-180 dataset when compared existing NN only achieved 68.2%, fuzzy classifier attains 63.1% and KNN classifier attains 67.3%.

Optimal Feature Subset Selection in High Dimensional Data Clustering

Feature subset selection is the process of identifying and removing many irrelevant and redundant features. Initially, the input micro array dataset is selected from the medical database. Then, preprocessing step is done in the input dataset. The resultant output is fed to the second step; here, the features are optimally selected using clustering and tree generation process. In our proposed technique, the modified kernel-based fuzzy c-means clustering algorithm with optimal minimum spanning tree algorithm is applied on the high dimensional dataset to select the important features, in which the optimal features are selected by means of binary cuckoo search. Next, the classification is done through neuro fuzzy classifier. At last, the experimentation is performed by means of different micro array dataset. The result proves that neuro fuzzy classifier outperformed the existing approach by attaining maximum accuracy of 89% for GLA-BRA-180 dataset when compared existing NN only achieved 68.2%, fuzzy classifier attains 63.1% and KNN classifier attains 67.3%.

Cost Optimization Using Hybrid Evolutionary Algorithm in Cloud Computing

The main aim of this research is to design the hybrid evolutionary algorithm for minimizing multiple problems of dynamic resource allocation in cloud computing. The resource allocation is one of the big problems in the distributed systems when the client wants to decrease the cost for the resource allocation for their task. In order to assign the resource for the task, the client must consider the monetary cost and computational cost. Allocation of resources by considering those two costs is difficult. To solve this problem in this study, we make the main task of client into many subtasks and we allocate resources for each subtask instead of selecting the single resource for the main task. The allocation of resources for the each subtask is completed through our proposed hybrid optimization algorithm. Here, we hybrid the Binary Particle Swarm Optimization (BPSO) and Binary Cuckoo Search algorithm (BCSO) by considering monetary cost and computational cost which helps to minimize the cost of the client. Finally, the experimentation is carried out and our proposed hybrid algorithm is compared with BPSO and BCSO algorithms. Also we proved the efficiency of our proposed hybrid optimization algorithm.

A Binary Cuckoo Search Algorithm for Graph Coloring Problem

The Graph Coloring Problem (GCP) is one of the most interesting, studied, and difficult combinatorial optimization problems. That is why several approaches were developed for solving this problem, including exact approaches, heuristic approaches, metaheuristics, and hybrid approaches. This paper tries to solve the graph coloring problem using a discrete binary version of cuckoo search algorithm. To show the feasibility and the effectiveness of the algorithm, it has used the standard DIMACS benchmark, and the obtained results are very encouraging.

An effective hybrid cuckoo search algorithm with improved shuffled frog leaping algorithm for 0-1 knapsack problems.

An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm.

Binary cuckoo search algorithm

In order to find a new algorithm to solve the NP-complete problem,the new efficient Cuckoo Search(CS) algorithm was improved into a Binary Cuckoo Search(BCS) algorithm by some means.These means include: binary coding strings were used to express the position of bird's nest,the path of Levy flights that cuckoos searched for new bird's nest was transformed into binary coding respectively according to Kennedy and Eberha's formula and Liu Jianghua's formula,and then a binary coding control factor was introduced to the hybrid update of the transformed binary coding,and the elimination mechanism of cuckoo eggs was reserved.The BCS algorithm performs better than genetic algorithm and some mixed genetic algorithms in solving the knapsack problem,and also better than genetic algorithm,ant colony optimization and particle swarm optimization in solving the traveling salesman problem,but slightly worse than the improved particle swarm optimization through adjusting the inertia weights adaptively.In solving the NP-complete problem,BCS algorithm is a new efficient algorithm.

A Multi-objective Binary Cuckoo Search for Bi-criteria Knapsack Problem

Cuckoo Search (CS) is one of the most recent population-based metaheuristics. CS algorithm is based on the cuckoo's behavior and the mechanism of Levy flights. The Binary Cuckoo Search algorithm (BCS) is new discrete version used to solve binary optimization problem based on sigmoid function. In this paper, we propose a new cuckoo search for binary multiobjective optimization. Pareto dominance is used to find optimal pareto solutions. Computational results on some bi-criteria knapsack instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm

Cuckoo search (CS) is one of the most recent population-based meta-heuristics. CS algorithm is based on the cuckoo’s behaviour and the mechanism of Lévy flights. Unfortunately, the standard CS algorithm is proposed only for continuous optimisation problems. In this paper, we propose a discrete binary cuckoo search (BCS) algorithm in order to deal with binary optimisation problems. To get binary solutions, we have used a sigmoid function similar to that used in the binary particle swarm optimisation algorithm. Computational results on some knapsack problem instances and multidimensional knapsack problem instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.