XOR-based artificial bee colony algorithm for binary optimization

The continuous artificial bee colony algorithm for binary optimization

Recently, many Computational-Intelligence algorithms have been proposed for solving continuous problem. The Differential Search Algorithm (DSA), a computational-intelligence based algorithm inspired by the migration movements of superorganisms, is developed to solve continuous problems. However, DSA proposed for solving problems with continuous search space proposed for solving should be modified for solving binary structured problems. When the DSA is intended for use in binary problems, continuous variables need to be converted into binary format due to solution space structure of this type of problem. In this study, the DSA is modified to solve binary optimization problems by using a conversion approach from continuous values to binary values. The new algorithm has been designated as the binary DSA or BDSA for short. First, when finding donors with the BDSA, four search methods (Bijective, Surjective, Elitist1 and Elitist2) with different iteration numbers are used and tested on 15 UFLP benchmark problems. The Elitist2 approach, which provides the best solution of the four methods, is used in the BDSA, and the results are compared with Continuous Particle Swarm Optimization (CPSO), Continuous Artificial Bee Colony (ABCbin), Improved Binary Particle Swarm Optimization (IBPSO), Binary Artificial Bee Colony (binABC) and Discrete Artificial Bee Colony (DisABC) algorithms using UFLP benchmark problems. Results from the tests and comparisons show that the BDSA is fast, effective and robust for binary optimization.

UTF: Upgrade transfer function for binary meta-heuristic algorithms

The sensor network design problem (SNDP) consists of the selection of the type, number and location of the sensors to measure a set of variables, optimizing a specified criteria, and simultaneously satisfying the information requirements. This problem is multimodal and involves several binary variables, therefore it is a complex combinatorial optimization problem. This paper presents a new Artificial Bee Colony (ABC) algorithm designed to solve high scale designs of sensor networks. For this purpose, the proposed ABC algorithm has been designed to optimize binary structured problems and also to handle constraints to fulfil information requirements. The classical version of the ABC algorithm was proposed for solving unconstrained and continuous optimization problems. Several extensions have been proposed that allow the classical ABC algorithm to work on constrained or on binary optimization problems. Therefore the proposed approach is a new version of the ABC algorithm that combines the binary and constrained optimization extensions to solve the SNDP. Finally the new algorithm is tested using different systems of incremental size to evaluate its quality, robustness, and scalability.

© 2015 Elsevier B.V. All rights reserved. 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.

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

© 2015 Elsevier B.V. All rights reserved. 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 xor-based artificial bee colony algorithm, called as binABC, is a novel variant of basic artificial bee colony (ABC) algorithm, which is proposed for solving binary optimization problems. This algorithm uses xor logic operator to search solution space instead of subtraction-based solution update rule of basic ABC due to discrete nature of the binary optimization. Similar to basic version of the algorithm, only one decision variable (dimension) is updated by the artificial agents of binABC. This approach causes slow convergence in the algorithm, and a proportional changing, which is depended on the number of decision variable of the optimization problem, is proposed in this study. The proposed approach is applied to solve a well-known binary optimization problem whose name is uncapacitated facility location problem (UFLP). Twelve instances of this problem are used in the experiments and obtained results are compared with the binABC algorithm in terms of solution quality, robustness and convergence characteristics. Experimental results show that the proposed approach is useful for controlling convergence characteristics and obtaining better quality of solution.

The performance of task scheduling algorithm in cloud computing determines the performance of the cloud system. This study mainly analyzed the application of the artificial bee colony (ABC) algorithm in the cloud task scheduling. In order to solve the problem of cloud task scheduling, the ABC algorithm was discretized to get the discrete artificial bee colony (DABC) algorithm. Then the mathematical model of cloud task scheduling was established and solved by the DABC algorithm. Finally, the simulation experiment was carried out, and the performance of first-come-first-served (FCFS), MIN–MIN, ABC and DABC algorithms under different cloud tasks was compared to verify the performance of the proposed algorithm. The results showed that the user waiting time of the DABC algorithm was 1210s, the load balance degree was 0.01, and the user payment fee was 1688 yuan when the number of cloud tasks was 500; compared with other algorithms, the user waiting time of the DABC algorithm was shorter, the resource load balance degree was higher, and the overall performance was better. The research results verify the effectiveness of the DABC algorithm in solving the problem of cloud task optimal scheduling, and it can be further extended and applied in practice.

This paper proposes an improved ABC algorithm to avoid the phenomenon of premature convergence of the basic artificial bee colony (ABC) algorithm. By combining the mutation operators of differential evolution (DE) algorithm, the new search equations of employed bees and onlookers are designed. Besides, a selective probability is leaded into the former one and a development coefficient is added in the latter one. In order to demonstrate the availability of the proposed ABC algorithm, simulations are conducted on a set of benchmark functions. The simulation results show that the improved ABC algorithm performs better than the basic ABC algorithm, DE algorithm and particle swarm optimization (PSO) algorithm.

DisABC: A new artificial bee colony algorithm for binary optimization

In this paper, artificial bee colony (ABC) algorithms are introduced to optimize ship hull forms for reduced drag. Two versions of ABC algorithm are used: one is the basic ABC algorithm, and the other is an improved artificial bee colony (IABC) algorithm. A recently developed fast flow solver based on the Neumann-Michell theory is used to evaluate the drag of the ship in the optimization process. The ship hull surface is represented by discrete triangular panels and modified using radial basis function interpolation method. The developed optimization algorithms are first validated by benchmark mathematical functions with different dimensions. They are then applied to the optimization of DTMB Model 5415 for reduced drag. Two optimal hull forms are obtained by the ABC and the IABC algorithms. A large drag reduction is obtained by both of the algorithms. The optimal hull form obtained by the IABC algorithm has larger drag reduction than that of the hull form from the ABC algorithm. The results show that two ABC algorithms can be used for optimizing ship hull forms and the IABC algorithm has better performance than the ABC algorithm for the tested case in ship hull form optimization.

The Artificial Bee Colony (ABC) algorithm is an active field of optimization based on swarm intelligence in recent years. Inspired by the mutation strategies used in Differential Evolution (DE) algorithm, this paper introduced three types strategies (“rand”,” best”, and “current-to-best”) and one or two numbers of disturbance vectors to ABC algorithm. Although individual mutation strategies in DE have been used in ABC algorithm by some researchers in different occasions, there have not a comprehensive application and comparison of the mutation strategies used in ABC algorithm. In this paper, these improved ABC algorithms can be analyzed by a set of testing functions including the rapidity of the convergence. The results show that those improvements based on DE achieve better performance in the whole than basic ABC algorithm. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3343

The quadratic assignment problem (QAP) is a well-known challenging combinational optimization problem that has received many researchers’ attention with varied real-world and industrial applications areas. Using the framework of basic artificial bee colony algorithm, frequently used crossover and mutation operators, and combined with an effective local search method, this paper proposes a simple but effective discrete artificial bee colony (DABC) algorithm for solving quadratic assignment problems (QAPs). Typical QAP benchmark instances are selected from QAPLIB in order to conduct the simulation experiment where common performance metrics are used to evaluate the algorithm. The paper also investigates the influence factors of the algorithm’s performance. The results show that the proposed algorithm is a quite effective and practical new approach for handling QAP problems.

Dynamic clustering with improved binary artificial bee colony algorithm