Abstract Artificial Bee Colony Research Articles

Power law-based local search in artificial bee colony

Artificial bee colony ABC optimisation algorithm is relatively a simple and recent population-based probabilistic approach for global optimisation. ABC has been outperformed over some nature inspired algorithms NIAs when tested over benchmark as well as real world optimisation problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is an enough chance to skip the true solution due to large step sizes. In order to balance the diversity and convergence capability of the ABC, in this paper, a power law-based local search strategy is proposed and integrated with ABC. The proposed strategy is named as power law-based local search in ABC PLABC. In the PLABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Further, to improve the exploration capability, numbers of scout bees are increased. The experiments on 24 test problems of different complexities show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest guided ABC GABC, best-so-far ABC BSFABC and modified ABC in most of the experiments.

A novel hybrid artificial bee colony algorithm with crossover operator for numerical optimization

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm intelligence algorithms which inspired by the foraging behavior of honey bee swarms. It has been widely used in numerical and engineering optimization problems. This paper presents a hybrid artificial bee colony (HABC) model to improve the canonical ABC algorithm. The main idea of HABC is to enhance the information exchange between bees by introducing the crossover operator of genetic algorithm to ABC. With suitable crossover operation, valuable information is fully utilized and it is expected that the algorithm can converge faster and more accurate. Eight versions of HABC algorithm combined by different selection and crossover methods under the model were proposed and tested on several benchmark functions. Then, the settings of the new parameter crossover rate for two well performed HABC versions are tested to verify their best values. Finally, four rotated functions and four shifted functions are used to test the performance of the two algorithms on complex functions and asymmetric functions. Experiment results showed that these two versions of HABC algorithm offer significant improvement over the original ABC and are superior to other two state of the art algorithms on some functions.

Two modified versions of artificial bee colony algorithm

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are modified and new configurations are used. The modified versions are tested on some well-known benchmark functions. Results show that the new changes have positive effects on the performance of ABC algorithm.

Enhancing artificial bee colony algorithm using inversely proportional mutation

Artificial bee colony (ABC) algorithm is a recently invented powerful optimiser. ABC has become very popular in swarm intelligence research area and has the advantages of its few control parameters, simplicity and ease of implementation. However, latest studies have been devoted to the improvement of the exploitation capability of the standard ABC, because ABC is good at exploration but poor at exploitation, and the convergence speed is also an issue in some cases. Motivated by these issues, this paper proposes a modified ABC algorithm that uses an inversely proportional mutation function and a new search mechanism to solve numerical function optimisation problems. The proposed algorithm is applied to a set of nine well–known benchmarks with different dimensions. To verify the performance of the proposed algorithm, it is compared with the standard ABC algorithm. Experimental results demonstrate that the proposed modified ABC algorithm performs much better than the standard ABC algorithm.

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

A modified artificial bee colony algorithm with its applications in signal processing

Artificial bee colony ABC algorithm is a biological-inspired optimisation algorithm proposed in recent years. It has been shown to have some advantages than most of conventional biological-inspired algorithms and has been widely used in many applications. However, the ABC algorithm does not consider the balance between global best and local best, and make ABC algorithm insufficiency. In this paper, a modified ABC algorithm is proposed, global best is introduced into the original ABC algorithm to modify the update equation of employed and onlooker bees while the equation for scouts remain unchanged. The effectiveness of the proposed approach is verified on the problem of peak-to-average power ratio reduction in orthogonal frequency division multiplexing signals and multi-level image segmentation. Simulation results showed that the proposed approach has better performance than traditional ABC algorithm with the same computational complexity.

Load Distribution Calculation of Pile Group Using Artificial Bee Colony Algorithm

Artificial bee colony algorithm (ABC) is a newly swarm intelligence optimization algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. The results of performances testing using three benchmark functions show that the numbers of evaluation for fitness function of ABC are obviously less than that using particle swarm optimization algorithm. Thus, ABC has better suitability for solving multi-modal optimization problems. Finally, ABC algorithm is applied to the load distribution calculation of pile group. The result shows that the ABC is feasible and has the advantages of high efficiency and easy implementation

Optimal Tuning of Robust Controller Based on Artificial Bee Colony Algorithm

Artificial bee colony algorithm (ABCA) is a novel swarm intelligence algorithm for global optimization. An efficient method based on ABCA is proposed for optimal tuning of robust proportional-integral-derivative (PID) controller in this paper. A multi-objective optimization is applied to balance several constraint factors of controller. Performance of robust PID controller is evaluated by a three-order and time-delay transfer function with 40%, 50% and 60% uncertainty, respectively. Simulation result clearly demonstrates that the designed controller can obtain an optimal tuning and endure parameter fluctuation. From comparisons of robust PID controller in different uncertainties, a conclusion can be obtained that performance of robust PID controller will be worse as the uncertainty increases, even obtain a divergent solution when the uncertainty is more than 60%.

Halton Based Initial Distribution in Artificial Bee Colony Algorithm and its Application in Software Effort Estimation

Artificial Bee Colony (ABC) is an optimization algorithm that simulates the foraging behavior of honey bees. It is a population based search technique whose performance depends largely on the distribution of initial population. Generally, uniform distributions are preferred since they best reflect the lack of knowledge about the optimum’s location. Moreover, these are easy to generate as most of the programming languages have an inbuilt function for generating uniformly distributed random numbers. However, in case of a population dependent optimization algorithm like that of ABC, random numbers having uniform probability distribution may not be a good choice as they may not be able exploit the search space fully. This paper uses quasi random numbers based on Halton sequence for the initial distribution and have compared the simulation results with initial population generated using uniform distribution. The proposed variant, termed as Halton based ABC (H-ABC), is validated on a set of 15 standard benchmark problems, 6 nontraditional shifted benchmark functions proposed at the special session of CEC2008, and has been used for solving the real life problem of estimating the cost model parameters. Numerical results indicate the competence of the proposed algorithm.

Chaotic Artificial Bee Colony Constrained Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. This paper presents Bee Swarm Optimization intended to introduce chaotic sequences into the algorithm. On the basis of Artificial Bee Colony Optimization Algorithm, a new algorithm by introducing constructing dynamic penalty function and chaotic sequences was presented. Based on Matlab software, the program CABCOA1.0 with hybrid discrete variables for the proposed algorithm was developed. The complex mechanical optimization was given. The results show that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence.

Artificial bee colony algorithm for fuzzy job shop scheduling

Artificial bee colony (ABC) is one of the most recently introduced swarm-based algorithms inspired by the intelligent foraging behaviour of honeybee swarm. In this study, ABC is applied to solve fuzzy job shop scheduling problems to investigate the advantage of ABC on fuzzy scheduling. In ABC, an operation-based coding and decoding procedure is adopted and neighbourhood structure based on insertion operator is proposed. In each cycle, the employed bee phase and the onlooker bee phase execute sequentially, no scouts are considered and the worst food source is replaced with the elite solution every certain cycles. ABC is applied to some instances and compared with the methods from the literature. Computational results show the promising advantage of ABC on fuzzy scheduling.

Differential Operators Embedded Artificial Bee Colony Algorithm

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In order to maintain a balance between these two antagonist factors, this paper suggests incorporation of differential evolution (DE) operators in the structure of basic ABC algorithm. The proposed algorithm called DE-ABC is validated on a set of 10 benchmark problems and the numerical results are compared with basic DE and basic ABC algorithm. The numerical results indicate that the presence of DE operators help in a significant improvement in the performance of ABC algorithm.

Artificial Bee Colony Algorithm for Reliability Analysis of Engineering Structures

Artificial bee colony algorithm is a noval optimization method which is inspired by bee colony foraging behavior. Its use in the structure reliability field presents not only the advantage of its facility of implementation, but also the capability to obtain the design point and the failure probability with good accuracy. And by this method, the reliability index of nonlinear and complex limit state function which iteration scheme may fail to converge could be obtained with efficiency. It is demonstrated by four examples that the present method is reliable and accurate in reliability analysis of engineering structures.