Binary Artificial Bee Colony Algorithm Research Articles

A novel binary artificial bee colony algorithm

Abstract This paper presents a novel artificial bee colony algorithm for binary optimization in general. Our proposal, named NBABC, features a mechanism which limits the number of dimensions that can be changed in the employed and onlookers bees’ phase. We compare the NBABC to other five binary variants of the continuous ABC, including the state-of-the-art versions for binary optimization, and other four well-known methods. We employ different types of tasks to assess the performance of all the algorithms including the OneMax problem, five variations of the 0/1 Knapsack problems and Feature Selection using eight public datasets. The experiments show that the new proposal obtained competitive results, and in some cases outperformed not only the binary-based ABCs but also the other binary swarm-based and evolutionary-based optimizers.

Feature selection with improved binary artificial bee colony algorithm for microarray data

In the areas of clinical diagnosis, gene expressions are known to have latent qualities as they denote the state of cells in molecular rankings. But the sample sizes are relatively small compared to the number of genes. Hence, the need to develop an efficient gene selection algorithm is appropriate to enhance predictive accuracy and as well prevent unfathomable conditions from the extensive quantity of genes. This article proposes an improved binary artificial bee colony algorithm (BABC) based on chaotic catfish effect for feature selection. Chaotic effect was added to the initialisation procedure of BABC, and further introduced chaotic catfish-bee for new nectar exploration, which can thus improve the BABC algorithm by preventing bees from getting trapped in a local optimum. The experiment shows that this new method indicated an elaborate feature simplification which achieved a very precise and significant accuracy of nine among the 11 datasets compared with other methods.

Binary Artificial Bee Colony Algorithm to Solve Single Objective Resource Allocation Problem

Binary Artificial Bee Colony Algorithm to Solve Single Objective Resource Allocation Problem

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

This paper presents a comparison between the Binary Artificial Bee Colony (BABC) and Binary Particle Swarm Optimization (BPSO) algorithm for structure selection of a Nonlinear Auto-Regressive Model (NAR) of the chaotic Mackey-Glass time-series data. Both stochastic optimization algorithms are swarm-based in nature with BABC mimicking bee colonies and BPSO mimicking the swarming behavior of birds. Recent research has suggested that the ABC algorithm has better solution quality compared to PSO. However, research on whether this advantage applies to the structure selection case in system identification has not been investigated. We conduct extensive tests to determine the convergence performance of both algorithms. It found that the BABC had managed to significantly outperform BPSO in terms of convergence consistency with a slight advantage in terms of solution quality.

Utilization-Oriented Spectrum Allocation in an Underlay Cognitive Radio Network

Spectrum access and assignment in cognitive radio networks (CRNs) are hot topics in wireless communications. Dynamic spectrum access and assignment could greatly improve spectrum resource efficiency and help to satisfy the explosively increasing communication demands of wireless devices. The problem of spectrum access and assignment in an underlay CRN is considered in this paper. The problem is modeled as a global optimization problem by considering the interference between primary and secondary users, the interference between secondary users and the utilization of the entire network. The utilization of the underlay CRN is maximized in the optimization model. To effectively solve this combinatorial optimization problem, a modified binary artificial bee colony algorithm is proposed. Numerical experiments are conducted to simulate the network and verify the proposed assignment method. The simulation results show that the proposed assignment method offers good performance in improving the spectrum usage efficiency and reducing the interference among primary and secondary users. Furthermore, the proposed algorithm is also very effective in achieving optimal allocation solutions compared with other methods.

A modified artificial bee colony approach for the 0-1 knapsack problem

The 0-1 knapsack problem (KP01) is one of the classical NP-hard problems in operation research and has a number of engineering applications. In this paper, the BABC-DE (binary artificial bee colony algorithm with differential evolution), a modified artificial bee colony algorithm, is proposed to solve KP01. In BABC-DE, a new binary searching operator which comprehensively considers the memory and neighbour information is designed in the employed bee phase, and the mutation and crossover operations of differential evolution are adopted in the onlooker bee phase. In order to make the searching solution feasible, a repair operator based on greedy strategy is employed. Experimental results on different dimensional KP01s verify the efficiency of the proposed method, and it gets superior performance compared with other five metaheuristic algorithms.

A novel binary artificial bee colony algorithm for the set-union knapsack problem

This article investigates how to employ artificial bee colony algorithm to solve Set-Union Knapsack Problem (SUKP). A mathematical model of SUKP, which is to be easily solved by evolutionary algorithms, is developed. A novel binary artificial bee colony algorithm (BABC) is also proposed by adopting a mapping function. Furthermore, a greedy repairing and optimization algorithm (S-GROA) for handling infeasible solutions by employing evolutionary technique to solve SUKP is proposed. The consolidation of S-GROA and BABC brings about a new approach to solving SUKP. Extensive experiments are conducted upon benchmark datasets for evaluating the performance of our proposed models. The results verify that the proposed approach is significantly superior to the baseline evolutionary algorithms for solving SUKP such as A-SUKP, ABCbin and binDE in terms of both time complexity and solution performance.

The Performance of Binary Artificial Bee Colony (BABC) in Structure Selection of Polynomial NARX and NARMAX Models

This paper explores the capability of the Binary Artificial Bee Colony (BABC) algorithm for feature selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) model, and compares its implementation with the Binary Particle Swarm Optimization (BPSO) algorithm. A binarized modification of the BABC algorithm was used to perform structure selection of the NARMAX model on a Flexible Robot Arm (FRA) dataset. The solution quality and convergence was compared with the BPSO optimization algorithm. Fitting and validation tests were performed using the One-Step Ahead (OSA), correlation and histogram tests. BABC was able to outperform BPSO in terms of convergence consistency with equal solution quality. Additionally, it was discovered that BABC was less prone to converge to local minima while BPSO was able to converge faster. Results from this study showed that BABC was better-suited for structure selection in huge dataset and the convergence has been proven to be more consistent relative to BPSO.

A binary artificial bee colony algorithm for constructing spanning trees in vehicular ad hoc networks

To accomplish reliable and efficient information routing, strong paths connecting all nodes are required in vehicular ad hoc networks (VANETs). Classical algorithms in graphic theory could find only one minimum spanning tree (MST) in VANETs. Swarm intelligence paradigms are able to obtain several alternatives to MST, which is useful for improving reliability of VANETs. This paper proposes a binary coded artificial bee colony (BABC) algorithm for tackling the spanning tree construction problem. A two-element variation technique is designed to keep the consistence of binary coded solutions. The proposed algorithm is applied to tackle a roadside-to-vehicle communication example. The success rate and average hitting time of the algorithm to find MST are also analyzed. It is found that the BABC algorithm could find MST with 92% probability. Though it is slower than Kruskal algorithm in terms of computational time, the BABC algorithm can attain several suboptimal spanning trees in one run. This suggests that the algorithm would be useful under the condition that tree paths are required to be rebuilt frequently while the network topology is unchanged in a short period.

Binary fish swarm algorithm for profit‐based unit commitment problem in competitive electricity market with ramp rate constraints

This study presents a new approach based on a binary fish swarm algorithm (BFSA) and dynamic economic dispatch (DED) method for generation companies (GENCOs) profit (PF)-based unit commitment (PBUC) problem considering power and reserve generations simultaneously in a day-ahead competitive electricity markets. BFSA is used to decide the units on/off status, whereas the optimum dispatch solution is determined using DED method with modified unit generation limits because of ramp rate constraints over the complete scheduled time horizon. To avoid the search to get trapped at a local optimal solution, swap move-based local search and cyclic re-initialisation operators are embedded in BFSA. Moreover, two strategies for selling power and reserve are considered in problem formulation and implementation phase. Its effectiveness is validated on 10 and 100 thermal units in a day-ahead electricity market in terms of GENCOs PF and computation time. The results obtained for PBUC problem with BFSA method have been compared with those obtained with priority list, dynamic programming, Lagrange relaxation, genetic algorithm and binary artificial bee colony algorithm, and BFSA has been found effective to achieve quality solutions in reasonable computation time.

A binary ABC algorithm based on advanced similarity scheme for feature selection

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers’ attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection.

The continuous artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABCbin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABCbin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABCbin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABCbin is an alternative and simple binary optimization tool in terms of solution quality and robustness.

BeeOWA: A novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles

In recent years, classifier ensembles have received increasing attention in the machine learning and pattern recognition communities. However, constructing classifier ensembles for one-class classification problems has still remained as a challenging research topic. To pursue this line of research, we need to address issues on how to generate a set of diverse one-class classifiers that are individually accurate and how to combine the outputs of them in an effective way. In this paper, we present BeeOWA, a novel approach to construct highly accurate one-class classifier ensembles. It uses a novel binary artificial bee colony algorithm, called BeePruner, to prune an initial one-class classifier ensemble and find a near-optimal sub-ensemble of base classifiers in a reasonable computational time. To evaluate the fitness of an ensemble solution, BeePruner uses two different measures: an exponential consistency measure and a non-pairwise diversity measure based on the Kappa inter-rater agreement. After one-class classifier pruning, BeeOWA uses a novel exponential induced OWA (ordered weighted averaging) operator, called EIOWA, to combine the outputs of base classifiers in the sub-ensemble. The results of experiments carried out on a number of benchmark datasets show that BeeOWA can outperform several state-of-the-art approaches, both in terms of classification performance and statistical significance.

A multi-objective placement of phasor measurement units using fuzzified artificial bee colony algorithm, considering system observability and voltage stability

This paper presents Multi-objective Optimal Placement of Phasor measurement units (MOPP) to improve the performance of power system monitoring and control. It is proposed for achieving complete observability with a minimal number of phasor measurement units (PMUs) and maximising the voltage stability level of the system, simultaneously. As the above mentioned objectives are conflicting in nature, a fuzzified binary artificial bee colony algorithm is considered to solve the MOPP problem to offer a good tradeoff solution between the competing objectives. Here, fuzzy membership for each objective function is designed and proposed to decide the best solution of MOPP problem. Initially, weak buses are identified using the fast voltage stability index calculation for their close and reliable monitoring. This is done in order to prevent the outage of these buses. The conventional rule is used to minimise the number of PMUs and a new rule is proposed to maximise both the observability and voltage stability levels of the system. The efficiency of the artificial bee colony method is validated on IEEE 14, 30 and 57 bus test systems and it is verified by comparing the performance of proposed method with earlier works.

A Novel Strategy-Based Hybrid Binary Artificial Bee Colony Algorithm for Unit Commitment Problem

In this paper, a hybrid approach based on a novel binary artificial bee colony (NBABC) algorithm and local search (LS) is developed to solve the unit commitment problem (UCP). Also, the ramp rate constraints are taken into account in the solution of UCP by performing conventional economic dispatch with modifying unit generating capacities over the entire scheduling time horizon. The proposed NBABC–LS method differs from its counterparts in three main aspects: (i) it utilizes a novel strategy which measures the dissimilarity between two binary strings for generating the new binary strings for UCP; (ii) it uses an intelligent scout bee phase; (iii) A LS module is hybridized with the NBABC algorithm. These modifications result in three major advantages: (i) it avoids the problem of slow and premature convergence and thus does not fall into the local optimum solutions; (ii) it can quickly find the near global optimum solution for UCP; (iii) accuracy and robustness of the solution are achieved. The proposed approach is successfully applied to the test systems up to 100 thermal units over 24-h scheduling time horizon and the real Turkish interconnected power system consisting of eight thermal units over 8-h scheduling time horizon. The obtained results confirm the quality solution in terms of total generation cost compared with the other methods reported in the literature.

A modified binary artificial bee colony algorithm for ramp rate constrained unit commitment problem

This paper proposes a new approach based on modified binary artificial bee colony (MBABC) algorithm and dynamic economic dispatch (DED) method for unit commitment problem (UCP). MBABC algorithm is used for committing/decommitting the thermal units, while optimum dispatch solution is determined using DED method. Proposed MBABC algorithm utilizes a new mechanism based on the measure of dissimilarity between binary strings for generating the new binary solutions for UCP. Moreover, in MBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The solution quality achieved by MBABC is enhanced by hybridizing the genetic crossover (GC) that provides the diversified search space. Performance of the proposed MBABC-GC algorithm is tested up to 300 thermal units over 24-hour time interval. The comparison of obtained results with other methods in the literature has confirmed the superiority of the MBABC-GC algorithm in terms of production cost and robustness. Copyright © 2015 John Wiley & Sons, Ltd.

Dynamic clustering with improved binary artificial bee colony algorithm

One of the most well-known binary (discrete) versions of the artificial bee colony algorithm is the similarity measure based discrete artificial bee colony, which was first proposed to deal with the uncapacited facility location (UFLP) problem. The discrete artificial bee colony simply depends on measuring the similarity between the binary vectors through Jaccard coefficient. Although it is accepted as one of the simple, novel and efficient binary variant of the artificial bee colony, the applied mechanism for generating new solutions concerning to the information of similarity between the solutions only consider one similarity case i.e. it does not handle all similarity cases. To cover this issue, new solution generation mechanism of the discrete artificial bee colony is enhanced using all similarity cases through the genetically inspired components. Furthermore, the superiority of the proposed algorithm is demonstrated by comparing it with the basic discrete artificial bee colony, binary particle swarm optimization, genetic algorithm in dynamic (automatic) clustering, in which the number of clusters is determined automatically i.e. it does not need to be specified in contrast to the classical techniques. Not only evolutionary computation based algorithms, but also classical approaches such as fuzzy C-means and K-means are employed to put forward the effectiveness of the proposed approach in clustering. The obtained results indicate that the discrete artificial bee colony with the enhanced solution generator component is able to reach more valuable solutions than the other algorithms in dynamic clustering, which is strongly accepted as one of the most difficult NP-hard problem by researchers.

A novel binary artificial bee colony algorithm based on genetic operators

This study proposes a novel binary version of the artificial bee colony algorithm based on genetic operators (GB-ABC) such as crossover and swap to solve binary optimization problems. Integrated to the neighbourhood searching mechanism of the basic ABC algorithm, the modification comprises four stages: (1) In neighbourhood of a (current) food source, randomly select two food sources from population and generate a solution including zeros (Zero) outside the population; (2) apply two-point crossover operator between the current, two neighbourhood, global best and Zero food sources to create children food sources; (3) apply swap operator to the children food sources to generate grandchildren food sources; and (4) select the best food source as a neighbourhood food source of the current solution among the children and grandchildren food sources. In this way, the global–local search ability of the basic ABC algorithm is improved in binary domain. The effectiveness of the proposed algorithm GB-ABC is tested on two well-known binary optimization problems: dynamic image clustering and 0–1 knapsack problems. The obtained results clearly indicate that GB-ABC is the most suitable algorithm in binary optimization when compared with the other well-known existing binary optimization algorithms. In addition, the achievement of the proposed algorithm is supported by applying it to the CEC2005 benchmark numerical problems.

Binary Artificial Bee Colony optimization using bitwise operation

Artificial Bee Colony (ABC) algorithm has been applied to many scientific and engineering problems for its easy of use and efficiency. However, the original ABC technique cannot be used in a binary search space directly. This paper proposes an effective binary ABC algorithm, or BitABC, that has a similar framework to the original ABC but uses a bitwise operation for the movements of the employed bees and onlooker bees. Experiments have been carried out by comparing BitABC with three binary ABC variants, DisABC, normABC, and BinABC, and the most used binary search technique, GA, on a set of 13 selected benchmark functions. Results show that the proposed BitABC performs better than, or at least comparable to, the other algorithms in terms of the final solution accuracy, convergence speed, and robustness.

Multi-user detector based on improved binary artificial bee colony algorithm

Optimum Multi-user Detection(OMD) technique can achieve the theoretical minimum error probability,but it has been proven to be a Non-deterministic Polynomial(NP) problem.As a new swarm intelligence algorithm,Artificial Bee Colony(ABC) algorithm has been widely used in various optimization problems.However,the traditional Binary Artificial Bee Colony(BABC) algorithm has the shortcomings of slower convergence speed and falling into local optimum easily.Concerning the shortcomings,an improved binary artificial bee colony algorithm was proposed and used for optimum multi-user detection.The initialization process was simplified.The one-dimensional-reversal neighborhood search strategy was adopted.Compared with optimum multi-user detection,the computation complexity of the improved algorithm declines obviously.The simulation results show that the proposed scheme has significant performance improvement over the conventional detection in anti-multiple access interference and near-far resistance.