Abstract Artificial Bee Colony Algorithm Research Articles

Parameter optimisation research for hydraulic turbine regulating system based on CGABC algorithm

Artificial bee colony (ABC) algorithm is regularly taken in the parameter optimisation of the hydraulic turbine regulating system (HTRS). The chaotic and global artificial bee colony (CGABC) algorithm is proposed to overcome the slow convergence speed and low convergence precision that existed in standard ABC algorithm. Among them, global optimal is used to enhance the global exploration ability, and chaos optimisation is adopted to increase the diversity of bee colony. The simulation results show that compared with fuzzy strategy, ZN, DE and ABC algorithm, the proposed CGABC algorithm significantly improves the dynamic transition process of HTRS. Simultaneously, the simulation experiment is carried out on HTRS with different governor parameters and controlled parameters when it is under the frequency and load disturbance condition. The results show that the dynamic characteristics and robustness is determined by the parameter selection, and the appropriate combination of parameters allows HTRS to achieve optimal dynamic performance and robustness.

A new emigrant utilization strategy for parallel artificial bee colony algorithm

Artificial bee colony (ABC) algorithm is one of the most important swarm intelligence based metaheuristics that models the foraging behavior of real honey bees. Like other swarm intelligence based optimization algorithms, ABC algorithm is intrinsically suitable for parallelization by using extensive computational power of the distributed or shared memory based architectures. In the vast majority of the studies, the whole bee colony is divided into equally sized subcolonies and evaluated concurrently for the parallelization purposes. However, when an algorithm is parallelized, some mechanisms should be modified or new techniques should be introduced. In this paper, a new emigrant creation utilization strategy also called swap model is introduced. The main idea lying behind the swap model is based on directly using the information sent by the topological neighbor to change the best solution of the current subcolony. For investigating possible contributions of the swap model on the performance of the parallel ABC algorithm, a set of experimental studies with different benchmark problems, number of subcolonies and migration periods was carried out. The results obtained from the experiments compared with the serial ABC algorithm and its some variants in addition to the conventional parallel implementation of the same algorithm. From the comparisons, it is concluded that the parallelization of the ABC with the swap model significantly improves the convergence speed of the algorithm while protecting the qualities of the solutions, speedup and efficiency values.

STATISTICALLY GUIDED ARTIFICIAL BEE COLONY ALGORITHM

Artificial Bee Colony algorithm is one of the naturally inspired meta heuristic method. As usual, in a meta heuristic method, intuitively appealing way to have better results is extending calculation time or increasing the fitness evaluation count. But the desired way is acquiring better results with less computation. So in this work a modified Artificial Bee Colony algorithm which can find better results with same computation is developed by benefiting statistical observations.

A novel artificial bee colony based on Gaussian sampling

Artificial bee colony (ABC) is an efficient optimization tool, which is inspired by swarm intelligence. Recent studies show that ABC is competitive to other famous bio-inspired optimization algorithms. However, ABC is good at global search, but poor at local search. To overcome this problem, a new ABC variant based on Gaussian sampling is proposed. Experiments are carried out on ten well-known benchmark functions. Computational results show that our approach outperforms the standard ABC and two other improved ABC algorithms.

Self-Adaptive Artificial Bee Colony for Function Optimization

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

Comparative analysis of selection schemes used in artificial bee colony algorithm

Artificial bee colony (ABC) algorithm, which has explicit strategies to balance intensification and diversification, is a simple and effective swarm intelligence algorithm for both continuous and combinatorial optimisation problems. Selection scheme, which is used by onlooker bees to select employed bees to follow, is an important factor for ABC algorithm to keep good balance between intensification and diversification. In this paper, a normalised fitness function for minimisation problem is first proposed for roulette wheel selection, so ABC can still have enough selection pressure in late stage. And then, aim to analyse the effect of different selection schemes, systematic experiments were carried for roulette wheel selection, rank selection, tournament selection, and disruptive selection on a set of test functions with different dimensions and different computation resources. Simulation results show that the suitability of a selection scheme depends not only on the features of test functions and but also on the computation resources used. No selection scheme can always outperform other selection schemes on all test functions.

A Novel Global ABC Algorithm with Self-Perturbing

Abstract Artificial bee colony (ABC) is a kind of a metaheuristic population-based algorithms proposed in 2005. Due to its simple parameters and flexibility, the ABC algorithm is applied to engineering problems, algebra problems, and so on. However, its premature convergence and slow convergence speed are inherent shortcomings. Aiming at the shortcomings, a novel global ABC algorithm with self-perturbing (IGABC) is proposed in this paper. On the basis of the original search equation, IGABC adopts a novel self-adaptive search equation, introducing the guidance of the global optimal solution. The search method improves the convergence precision and the global search capacity. An excellent leader can lead the whole team to obtain more success. In order to obtain a better “leader,” IGABC proposes a novel method with global self-perturbing. To avoid falling into the local optimum, this paper designed a new mutation strategy that simulates the natural phenomenon of sick fish being eaten.

An improved artificial bee colony algorithm for global numerical optimisation

Artificial bee colony (ABC) is a simple and powerful optimisation algorithm, which simulates the random behaviour of honey bees. In this study, a modified version of ABC algorithm is proposed by considering: 1) initialising the population based on chaos theory; 2) utilising multiple searches, instead of single search, in employee and onlooker bee phases; 3) controlling the frequency of perturbation by a modification rate. The proposed algorithm implemented by C# programming language and executed on benchmark functions of Sphere, Rosenbrock, Rastrigin, non-continuous Rastrigin, Griewank, Schwefel, Schwefel 1.2, Schwefel 2.2, Step and Ackley. The performance of proposed ABC algorithm is compared with ABC and modified ABC algorithms, on different tests, according to measures of mean and standard deviation. Findings showed the suggested algorithm outperforms ABC and modified ABC algorithms in 65% of test cases in getting best mean and in 55% of cases for standard deviation.

Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

Hybridizing Artificial Bee Colony Algorithm with Multi-Parent Crossover Operator

Artificial Bee Colony algorithm (ABC) is a new optimization algorithms used to solve several optimization problems. The algorithm is a swarm-based that simulates the intelligent behavior of honey bee swarm in searching for food sources. Several variations of ABC have been three existing solution vectors, the new solution vectors will replace the worst three vectors in the food source proposed to enhance its performance. This paper proposes a new variation of ABC that uses multi-parent crossover named multi parent crossover operator artificial bee colony (MPCO-ABC). In the proposed technique the crossover operator is used to generate three new parents based on memory (FSM). The proposed algorithm has been tested using a set of benchmark functions. The experimental results of the MPCO-ABC are compared with the original ABC, GABC. The results prove the efficiency of MPCO-ABC over ABC. Another comparison of MPCO-ABC results made with the other variants of ABC that use crossover and/or mutation operator. The MPCO-ABC almost always shows superiority on all test functions.

A hybrid artificial bee colony based on differential evolution for production scheduling problems

Artificial bee colony (ABC) is a new optimisation technique, which has been successfully applied to various optimisation problems. In this paper, we propose a new ABC algorithm called ABCDE, which utilises the differential evolution (DE) scheme. It is known that both employed and onlooker bees in ABC use the same solution search strategy to generate new candidate solutions. In our new approach ABCDE, the employed bees still use the original solution search strategy, while the onlooker bees employ the DE scheme. Experimental studies are conducted on a set of recently proposed CEC 2014 benchmark functions. Computational results show the good search abilities of ABCDE. To further verify the performance of ABCDE on discrete optimisation problems, we apply ABCDE to solve production scheduling problems. Simulation results demonstrate that our approach can achieve promising solutions.

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.

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%.

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.

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.