DisABC: A new artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm is one of the recently proposed swarm intelligence based algorithms for continuous optimization. Therefore it is not possible to use the original ABC algorithm directly to optimize binary structured problems. In this paper we introduce a new version of ABC, called DisABC, which is particularly designed for binary optimization. DisABC uses a new differential expression, which employs a measure of dissimilarity between binary vectors in place of the vector subtraction operator typically used in the original ABC algorithm. Such an expression helps to maintain the major characteristics of the original one and is respondent to the structure of binary optimization problems, too. Similar to original ABC algorithm, DisABC's differential expression works in continuous space while its consequence is used in a two-phase heuristic to construct a complete solution in binary space. Effectiveness of DisABC algorithm is tested on solving the uncapacitated facility location problem (UFLP). A set of 15 benchmark test problem instances of UFLP are adopted from OR-Library and solved by the proposed algorithm. Results are compared with two other state of the art binary optimization algorithms, i.e., binDE and PSO algorithms, in terms of three quality indices. Comparisons indicate that DisABC performs very well and can be regarded as a promising method for solving wide class of binary optimization problems.     

Tent 混沌人工蜂群与粒子群混合算法

针对人工蜂群和粒子群算法的优势与缺陷,提出一种Tent混沌人工蜂群粒子群混合算法.首先利用Tent混沌反向学习策略初始化种群;然后划分双子群,利用Tent混沌人工蜂群算法和粒子群算法协同进化;最后应用重组算子选择最优个体作为跟随蜂的邻域蜜源和粒子群的全局极值.仿真结果表明,该算法不仅能有效避免早熟收敛,而且能有效跳出局部极值,与其他最新人工蜂群和粒子群算法相比具有较强的全局搜索能力和局部搜索能力.     

A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems

This paper presents a hybridization of particle swarm optimization (PSO) and artificial bee colony (ABC) approaches, based on recombination procedure. The PSO and ABC are population-based iterative methods. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents (employed, onlooker and scout bees) of the ABC do not directly use this information but the global best solution in the ABC is stored at the each iteration. The global best solutions obtained by the PSO and ABC are used for recombination, and the solution obtained from this recombination is given to the populations of the PSO and ABC as the global best and neighbor food source for onlooker bees, respectively. Information flow between particle swarm and bee colony helps increase global and local search abilities of the hybrid approach which is referred to as Hybrid approach based on Particle swarm optimization and Artificial bee colony algorithm, HPA for short. In order to test the performance of the HPA algorithm, this study utilizes twelve basic numerical benchmark functions in addition to CEC2005 composite functions and an energy demand estimation problem. The experimental results obtained by the HPA are compared with those of the PSO and ABC. The performance of the HPA is also compared with that of other hybrid methods based on the PSO and ABC. The experimental results show that the HPA algorithm is an alternative and competitive optimizer for continuous optimization problems.     

The parameter extraction of the thermally annealed Schottky barrier diode using the modified artificial bee colony

In this paper, a new method based on the modified artificial bee colony (MABC) algorithm to determine the main characteristic parameters of the Schottky barrier diode such as barrier height, ideality factor and series resistance. For this model, the Ni/n-GaAs/In Schottky barrier diode was produced and annealed at different temperature in a laboratory. The performance of the modified ABC method was compared to that of the basic artificial bee colony (ABC), particle swarm optimization (PSO), differential evolution (DE), genetic algorithm (GA) and simulated annealing (SA). From the results, it is concluded that the modified ABC algorithm is more flexible and effective for the parameter determination than the other algorithms.     

求解工程约束优化问题的PSO-ABC混合算法*

针对包含约束条件的工程优化问题,提出了基于人工蜂群的粒子群优化PSO-ABC算法。将PSO中较优的粒子作为ABC算法的蜜源,并使用禁忌表存储其局部极值,克服粒子群优化算法易陷入局部最优的缺陷。采用可行性规则进行约束处理,将粒子种群分为可行子群和不可行子群,并在ABC算法产生蜜源的过程中保留部分较优的可行解和不可行解的信息,弥补了可行性规则处理最优点位于约束边界附近的问题时存在的不足。四个典型工程优化设计的实验结果表明,该算法能够寻得更优的约束最优化解,且稳健性更强。     

冯诺依曼人工蜂群算法求解RFID网络优化问题

本文提出了一种具有冯诺依曼社会结构的新型人工蜂群算法(VNABC)。本文采用四个测试函数验证VNABC算法性能,并将其应用于求解射频识别系统中的读写器网络覆盖和防冲突问题。试验结果表明,与基本人工蜂群算法和粒子群优化算法比较,VNABC算法求解复杂优化问题收敛速度较快、求解精度更高,从而为应用智能方法求解RFID系统优化问题提供了有效的可行方案。     

基于动态整体更新和试探机制的蜂群算法

蜂群算法已被证明其效率高于多数传统优化算法,但是对于不可分离变量的函数则优势不明显。为平衡单维更新与整体更新,避免算法在某一方面开采过深陷入局部最优,通过计算单维开采成功率动态地控制参数limit,提出了一种单维更新和整体更新交替进行的混合算法。该算法在整体更新阶段采用基于试探机制的粒子群算法,避免种群飞向错误的方向。采用多种不同类型的基准函数对改进算法进行测试,数值实验结果验证了该算法的有效性。     

人工蜂群算法在重力坝断面优化设计中的应用

人工蜂群算法是一种新型的群智能优化算法,对于处理复杂的非线性多峰值优化问题具有很好的适用性。对三种典型测试函数进行性能测试,与粒子群优化算法相比较,人工蜂群算法的适应度函数评价次数明显较少,对求解多峰值优化问题具有较好的适应性,将人工蜂群算法应用于重力坝断面优化设计,研究结果表明,该方法是可行的,具有寻优效率高且易于实现的优点。     

基于精英蜂群搜索策略的人工蜂群算法

针对人工蜂群(ABC)算法存在收敛速度慢、求解精度不高、容易陷入局部最优等问题,利用蜂群觅食过程中先由侦察蜂进行四处侦察食物,并利用蜂群搜索构建精英群体指导蜂群觅食寻优。据此,提出了一种模拟侦察蜂侦察觅食行为的基于精英蜂群搜索策略的连续优化算法。算法利用构建精英蜂群策略、改进侦察蜂搜索机制以及基于目标函数值选择寻优三个主要策略加强算法的搜索机制。数值实验表明,所提算法不仅寻优精度和寻优率非常高,且收敛速度快,并能适于高维空间的优化问题。     

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.     

Artificial bee colony algorithm for small signal model parameter extraction of MESFET

This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16—element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5–25 GHz.     

Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization

The artificial bee colony has the advantage of employing fewer control parameters compared with other population-based optimization algorithms. In this paper a binary artificial bee colony (BABC) algorithm is developed for binary integer job scheduling problems in grid computing. We further propose an efficient binary artificial bee colony extension of BABC that incorporates a flexible ranking strategy (FRS) to improve the balance between exploration and exploitation. The FRS is introduced to generate and use new solutions for diversified search in early generations and to speed up convergence in latter generations. Two variants are introduced to minimize the makepsan. In the first a fixed number of best solutions is employed with the FRS while in the second the number of the best solutions is reduced with each new generation. Simulation results for benchmark job scheduling problems show that the performance of our proposed methods is better than those alternatives such as genetic algorithms, simulated annealing and particle swarm optimization.     

An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

This paper investigates a waste collection problem with the consideration of midway disposal pattern. An artificial bee colony (ABC)-based hybrid approach is developed to handle this problem, in which the hybrid ABC algorithm is proposed to generate the better optimum-seeking performance while a heuristic procedure is proposed to select the disposal trip dynamically and calculate the carbon emissions in waste collection process. The effectiveness of the proposed approach is validated by numerical experiments. Experimental results show that the proposed hybrid approach can solve the investigated problem effectively. The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four popular metaheuristics, namely a genetic algorithm, a particle swarm optimization algorithm, an enhanced ABC algorithm and a hybrid particle swarm optimization algorithm. It is also found that the midway disposal pattern should be used in practice because it reduces the carbon emission at most 7.16% for the investigated instances.     

An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems

Artificial bee colony (ABC) algorithm developed by Karaboga is a nature inspired metaheuristic based on honey bee foraging behavior. It was successfully applied to continuous unconstrained optimization problems and later it was extended to constrained design problems as well. This paper introduces an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. Our UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm. This upgraded algorithm has been implemented and tested on standard engineering benchmark problems and the performance was compared to the performance of the latest Akay and Karaboga’s ABC algorithm. Our numerical results show that the proposed UABC algorithm produces better or equal best and average solutions in less evaluations in all cases.     

The improvement of glowworm swarm optimization for continuous optimization problems

Glowworm swarm optimization (GSO) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to enhances accuracy and convergence rate of the GSO, two strategies about the movement phase of GSO are proposed. One is the greedy acceptance criteria for the glowworms update their position one-dimension by one-dimension. The other is the new movement formulas which are inspired by artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). To compare and analyze the performance of our proposed improvement GSO, a number of experiments are carried out on a set of well-known benchmark global optimization problems. The effects of the parameters about the improvement algorithms are discussed by uniform design experiment. Numerical results reveal that the proposed algorithms can find better solutions when compared to classical GSO and other heuristic algorithms and are powerful search algorithms for various global optimization problems.     

Bollinger bands approach on boosting ABC algorithm and its variants

In this study, a new algorithm that will improve the performance and the solution quality of the ABC (artificial bee colony) algorithm, a swarm intelligence based optimization algorithm is proposed. ABC updates one parameter of the individuals before the fitness evaluation. Bollinger bands is a powerful statistical indicator which is used to predict future stock price trends. By the proposed method an additional update equation for all ABC-based optimization algorithms is developed to speed up the convergence utilizing the statistical power of the Bollinger bands. The proposed algorithm was tested against classical ABC algorithm and recent ABC variants. The results of the proposed method show better performance in comparison with ABC-based algorithm with one parameter update in convergence speed and solution quality.     

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.     

引入人工蜂群搜索算子的粒子群算法

针对标准粒子群算法易出现早熟现象和收敛速度慢等问题,提出一种引入人工蜂群搜索算子的粒子群算法.首先利用人工蜂群搜索算子很强的探索能力,对粒子搜索到的历史最优位置进行搜索以帮助算法快速跳出局部最优点;然后,为了提高算法的全局收敛速度,提出一种基于混沌和反学习的初始化方法.通过12个标准测试函数的仿真实验并与其他算法相比较,所得结果表明所提出的算法具有较快的收敛速度和很强的跳出局部最优的能力.     

BMNABC: Binary Multi-Neighborhood Artificial Bee Colony for High-Dimensional Discrete Optimization Problems

Abstract

Many meta-heuristic algorithms have been proposed to solve continuous optimization problems. Hence, researchers have applied various techniques to change these algorithms for discrete search spaces. Artificial bee colony (ABC) algorithm is one of the well-known algorithms for real search spaces. ABC has a good ability in exploration but it is weak in exploitation. Several binary versions of ABC have been proposed so far. Since the methods are based on the standard ABC, they have the disadvantage of ABC. In this article, a new binary ABC called binary multi-neighborhood ABC (BMNABC) has been introduced to enhance the exploration and exploitation abilities in the phases of ABC. BMNABC applies the near and far neighborhood information with a new probability function in the first and second phases. A more conscious search than the standard ABC is done in the third phase for those solutions which have been not improved in the previous phases. The performance of algorithm has been evaluated by low- and high-dimensional functions and the 0-1 multidimensional knapsack problems. The proposed method has been compared with state-of-the-art algorithms. The results showed that BMNABC had a better performance in terms of solution accuracy and convergence speed.     

Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO), the Fast Otsu’s method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods.