Improved Artificial Bee Colony Algorithm Research Articles

Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

The artificial bee colony (ABC) algorithm is a recently introduced optimization method in the research field of swarm intelligence. This paper presents an improved ABC algorithm named as OGABC based on opposition-based learning (OBL) and global best search equation to overcome the shortcomings of the slow convergence rate and sinking into local optima in the process of inversion of atmospheric duct. Taking the inversion of the surface duct using refractivity from clutter (RFC) technique as an example to validate the performance of the proposed OGABC, the inversion results are compared with those of the modified invasive weed optimization (MIWO) and ABC. The radar sea clutter power calculated by parabolic equation method using the simulated and measured refractivity profile is utilized to carry out the inversion of the surface duct, respectively. The comparative investigation results indicate that the performance of OGABC is superior to that of MIWO and ABC in terms of stability, accuracy, and convergence rate during the process of inversion.

Optimal operation of energy at hydrothermal power plants by simultaneous minimization of pollution and costs using improved ABC algorithm

The aim of this paper is simultaneous minimization of hydrothermal units to reach the best solution by employing an improved artificial bee colony (ABC) algorithm in a multi-objective function consisting of economic dispatch (ED) considering the valve-point effect and pollution function in power systems in view of the hot water of the hydro system. In this type of optimization problem, all practical constraints of units were taken into account as much as possible in order to comply with the reality. These constraints include the maximum and minimum output power of units, the constraints caused by the balance between supply and demand, the impact of pollution, water balance, uneven production curve considering the valve-point effect and system losses. The proposed algorithm is applied on the studied system, and the obtained results indifferent operating conditions are analyzed. To investigate in various operating conditions, different load profiles in 12 h are taken into account. The obtained results are compared with those of the other methods including the genetic algorithm (GA), the Basu technique, and the improved genetic algorithm. Fast convergence is one of this improved algorithm features.

An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC)-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining “replace” and “alter” operations, and the third is a “swap” strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China), and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing) was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

An Artificial Bee Colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization

Artificial Bee Colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees’ search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that the our proposed algorithm is effective and robust and outperform than other algorithms.

A new improved artificial bee colony algorithm for ship hull form optimization

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

An improved artificial bee colony algorithm for solving constrained optimization problems

The artificial bee colony (ABC) algorithm is a global stochastic optimization algorithm inspired by simulating the foraging behavior of honey bees. It has been successfully applied to solve the constrained optimization problems (COPs) with a constraint handling technique (Deb’s rules). However, it may also lead to premature convergence. In order to improve this problem, we propose an improved artificial bee colony (I-ABC) algorithm for COPs. In I-ABC algorithm, we firstly relax the Deb’s rules by introducing the approximate feasible solutions to suitably utilize the information of the infeasible solutions with better objective function value and small violation. Next, we construct a selection strategy based on rank selection and design a search mechanism using the information of the best-so-far solution to balance the exploration and the exploitation at different stages. In addition, periodic boundary handling mode is used to repair invalid solutions. To verify the performance of I-ABC algorithm, 24 benchmark problems are employed and two comparison experiments have been carried out. The numerical results show that the proposed I-ABC algorithm has an outstanding performance for the COPs.

Improved Artificial Bee Colony Algorithm and Application in Path Planning of Crowd Animation

Crowd animation is a new and continuous challenge in computer animation. In tradition, crowd animation can be realized by key frame technology, and animators should set every character’s expression, action, motion, and behavior. Therefore animators’ workload by hand will increase tremendously with characters growing, which lead to difficult to realize crowd animation for low efficiency and poor global controllability, especially in path planning by appointing a target position for each individuals. In order to overcome these, an improved artificial bee colony (IM-ABC) algorithm is proposed to apply on the path planning of crowd animation. The IM-ABC is fit to simulate the crowd motion in animation based on the following two merits over the others in crowd animation. One is the rule of role transformation, which can make the rapid convergence of the result and avoid getting trapped in the local optima. The other is the realization of multi-object optimization in the process of iteration, which reaches the uniformly distributed result of swarm motion and especially fits to realize the path plan. In this paper, we simply reviews classical ABC algorithm proposed by Karaboga at the beginning. Then, in order to speed the convergence and make individuals generate paths more realistic and natural, some measures are taken to modify the classical ABC (called IM-ABC) algorithm, which include initializing colony based on chaos sequence, selfadaptively selecting the follower bees, and adaptively controlling parameters, etc. After the experiments of benchmark functions, the results confirm that the IM-ABC have better performance than the classical ABC algorithm and others. Finally, the IM-ABC algorithm is used for path planning to generate the route from the initial to the destination without collision. Through simulation experiments based on four motion models it is showed that this method can succeed generating the optimum paths with efficiency, intelligence, and natural features.

An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

Optimal power flow (OPF) objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC) algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

An improved artificial bee colony with new search strategy

Artificial Bee Colony ABC is a recently proposed optimisation algorithm, which is inspired by the foraging behaviour of honey bees. Although the original ABC has obtained competitive performance on many optimisation problems, it lacks exploitation ability. To overcome this issue, an improved ABC algorithm called IABC is proposed in this paper. The IABC uses a new search strategy by incorporating some randomly selected individuals and the global best individual. In the experiment, a set of well-known benchmark functions are tested. Computational results show that the proposed IABC outperforms the original ABC and GABC.

Improved artificial bee colony algorithm based gravity matching navigation method.

Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.

Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set

This paper proposes a novel synthetic aperture radar (SAR) image segmentation algorithm based on the neutrosophic set (NS) and improved artificial bee colony (I-ABC) algorithm. In this algorithm, threshold value estimation is considered as a search procedure that searches for a proper value in a grayscale interval. Therefore, I-ABC optimization algorithm is presented to search for the optimal threshold value. In order to get an efficient and powerful fitness function for I-ABC algorithm, the input SAR image is transformed into the NS domain. Then, a neutrosophic T and I subset images are obtained. A co-occurrence matrix based on the neutrosophic T and I subset images is constructed, and two-dimensional gray entropy function is described to serve as the fitness function of I-ABC algorithm. Finally, the optimal threshold value is quickly explored by the employed, onlookers and scouts bees in I-ABC algorithm. This paper contributes to SAR image segmentation in two aspects: (1) a hybrid model, having two different feature extraction methods, is proposed. (2) An optimal threshold value is automatically selected by maximizing the separability of the classes in gray level image by incorporating a simple and fast search strategy. The effectiveness of the proposed algorithm is demonstrated by application to real SAR images.

Biological edge detection for UCAV via improved artificial bee colony and visual attention

Purpose – The purpose of this paper is to propose a biological edge detection approach for aircraft such as unmanned combat air vehicle (UCAV), with the objective of making the UCAV recognize targets, especially in complex noisy environment. Design/methodology/approach – The hybrid model of saliency-based visual attention and artificial bee colony (ABC) algorithm is established for edge detection of UCAV. Visual attention can extract the region of interesting objects, and this approach can narrow the searching region for object segmentation, which can reduce the computational complexity. An improved ABC algorithm is applied in edge detection of the salient region. Findings – This work improved ABC algorithm by modifying the search strategy and adding some limits, so that it can be applied to edge detection problem. A hybrid model of saliency-based visual attention and ABC algorithm is developed. Experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee ef...

A method for axis straightness error evaluation based on improved artificial bee colony algorithm

An improved artificial bee colony (IABC) optimization algorithm for the accurate evaluation of minimum zone axis straightness error from a set of coordinate measurement data points was proposed. In the proposed algorithm, the opposition-based learning method was employed to produce initial population and scouts, the employed bees used greedy selection mechanism to update the best food source achieved so far one by one, and a new search mechanism inspired by differential evaluation was used for onlookers. The nonlinear mathematical model for axis straightness error evaluation and the fitness function of IABC were introduced in detail. Four classical test functions were selected in the experiments; the simulation results verified the feasibility of IABC algorithm. According to two practical examples, the results obtained by the IABC algorithm are more accurate and efficient than other conventional methods. It is a unified approach for other form and position error evaluations and is well suited for high-precision measuring equipments such as the CMM.

Improved Artificial Bee Colony Algorithm and Its Application in LQR Controller Optimization

In order to get the optimal performance of controller and improve the design efficiency, artificial bee colony (ABC) algorithm as a metaheuristic approach which is inspired by the collective foraging behavior of honey bee swarms is considered for optimal linear quadratic regulator (LQR) design in this paper. Furthermore, for accelerating the convergence speed and enhancing the diversities of population of the traditional ABC algorithm, improved solution searching approach is proposed creatively. The proposed approach refers to the procedure of differential mutation in differential evolutionary (DE) algorithm and produces uniform distributed food sources in employed bee phase to avoid local optimal solution. Meanwhile, during the onlooker bees searching stage where the solution search area has been narrowed by employed bees, new solutions are generated around the solution with higher fitness value to keep the fitness values increasing monotonously. The improved ABC algorithm is applied to the optimization of LQR controller for the circular-rail double inverted pendulum system, and the simulation results show the effect on the proposed optimization problem.

Artificial Bee Colony Optimization Embedded with Simulated Annealing for the Combined Heat and Power Economic Dispatch Problem

This paper presents a new approach for solving the Combined Heat and Economic Dispatch (CHPED) problem using an integrated algorithm based on artificial bee colony algorithm (ABC) and Simulated Annealing (SA). Artificial Bee Colony algorithm (ABC) is inspired by the foraging behavior of honey bee swarm, is a biological inspired optimization. It shows more effective than the other optimization algorithms. However, ABC is good at exploration but poor at exploitation, and its convergence speed is also slow in some cases. To overcome this deficiency, this paper proposes an improved ABC algorithm called ABC-SA algorithm. In this algorithm ABC is acting as a base level search to direct the search towards the optima region and local searches synergistically combined with simulated Annealing (SA). The performance of the proposed algorithm (ABC-SA) is validated by illustration with test system. The results of the proposed algorithm are compared with those of Practical Swarm Optimization (PSO), ABC, Real –Coded Genetic Algorithm (RCGA), Bee Colony Optimization (BCO), SA and Evolutionary Programming techniques (EP). From numerical results, it is seen that the proposed algorithm is able to provide a better solution at a lesser computational effort.

A Modified ABC Algorithm & Its Application to Wireless Sensor Network Dynamic Deployment

Wireless Sensor Networks plays a gargantuan role in instrumenting the modern day world; hence it is crucial to develop an optimal design flow. Out of the various Swarm intelligent algorithms developed so far, Artificial Bee Colony (ABC) algorithm offers a vivid scope for this objective. First introduced for solving numerical optimization problems, ABC has now spread across almost every phase of WSN construction. In this study, an improved ABC algorithm has been proposed to match the different characteristics of wireless sensor network deployment process, which will be optimum for real time dynamic network functioning. KEYWORDS: Wireless Sensor Networks, Swarm Intelligent Algorithms, scouts, foragers, onlookers, waggle dance, self organization, Opposition-based learning.

Improved artificial bee colony algorithm with mutual learning

The recently invented artificial bee colony (ABC) algorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of finding a neighboring food source. This paper presents an improved ABC algorithm with mutual learning (MutualABC) that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The performance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algorithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.

An improved artificial bee colony algorithm for the blocking flowshop scheduling problem

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obtain solutions for the scout bees. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. The IABC is compared with the existing hybrid discrete differential evolution and discrete artificial bee colony algorithms based on the well-known flowshop benchmark of Taillard. The computational results and comparison demonstrate the superiority of the proposed IABC algorithm for the blocking flowshop scheduling problems with makespan criterion.

Development and investigation of efficient artificial bee colony algorithm for numerical function optimization

Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, we propose an improved ABC algorithm called I-ABC. In I-ABC, the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process. Inertia weight and acceleration coefficients are defined as functions of the fitness. In addition, to further balance search processes, the modification forms of the employed bees and the onlooker ones are different in the second acceleration coefficient. Experiments show that, for most functions, the I-ABC has a faster convergence speed and better performances than each of ABC and the gbest-guided ABC (GABC). But I-ABC could not still substantially achieve the best solution for all optimization problems. In a few cases, it could not find better results than ABC or GABC. In order to inherit the bright sides of ABC, GABC and I-ABC, a high-efficiency hybrid ABC algorithm, which is called PS-ABC, is proposed. PS-ABC owns the abilities of prediction and selection. Results show that PS-ABC has a faster convergence speed like I-ABC and better search ability than other relevant methods for almost all functions.

Improved artificial bee colony algorithm for global optimization

The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely “ ABC/best/1” and “ ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing “ ABC/rand/1” and “ ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.