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1.
Artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions.  相似文献   

2.
A modified artificial bee colony algorithm   总被引:5,自引:0,他引:5  
Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose an improved solution search equation, which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation. Then, in order to make full use of and balance the exploration of the solution search equation of ABC and the exploitation of the proposed solution search equation, we introduce a selective probability P and get the new search mechanism. In addition, to enhance the global convergence, when producing the initial population, both chaotic systems and opposition-based learning methods are employed. The new search mechanism together with the proposed initialization makes up the modified ABC (MABC for short), which excludes the probabilistic selection scheme and scout bee phase. Experiments are conducted on a set of 28 benchmark functions. The results demonstrate good performance of MABC in solving complex numerical optimization problems when compared with two ABC-based algorithms.  相似文献   

3.
Artificial bee colony (ABC) algorithm is a very popular population-based algorithm. Unfortunately, there exists a shortcoming of slow convergence rate, which partly results from random choices of neighbor individuals regarding its solution search equation. A novel scheme for the choice of neighbors is introduced based on grey relational degrees between a current individual and its neighbors to overcome the insufficiency. Then, the chosen neighbor is used to guide the search process. Additionally, inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed. What is more, solution search equations using information of the best individual, an opposition-based learning method and a chaotic initialization technique are also integrated into the proposed algorithm called grey artificial bee colony algorithm (GABC for short). Subsequently, the effectiveness and efficiency of GABC are validated on a test suite composed of fifty-seven benchmark functions. Furthermore, it is also compared with a few state-of-the-art algorithms. The related experimental results show the effectiveness and superiority of GABC.  相似文献   

4.
In this paper, we put forward a hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation. The bee life-cycle model is firstly constructed, which means that each individual can reproduce or die dynamically throughout the searching process and population size can dynamically vary during execution. With the comprehensive learning, the bees incorporate the information of global best solution into the search equation for exploration, while the Powell’s search enables the bees deeply to exploit around the promising area. Finally, we instantiate a hybrid artificial bee colony (HABC) optimizer based on the proposed model, namely HABC. Comprehensive test experiments based on the well-known CEC 2014 benchmarks have been carried out to compare the performance of HABC against other bio-mimetic algorithms. Our numerical results prove the effectiveness of the proposed hybridization scheme and demonstrate the performance superiority of the proposed algorithm.  相似文献   

5.
为了提高人工蜂群(ABC)算法的局部搜索能力,加快其收敛速度,将Rosenbrock转轴搜索的方法引入ABC算法,提出了一种转轴ABC算法.该算法每隔一定的迭代次数,就在ABC算法找到的当前极值的邻域内用Rosenbrock方法进行一次转轴搜索,以引导算法找到函数值下降最快的方向.此外,新算法利用对立策略对算法随机产生的初始种群进行调整,得到了质量较高的初始种群.通过对几个标准测试函数的性能测试,验证了算法的快速收敛性和稳定性,说明对其的改进是可行且有效的.  相似文献   

6.
李国亮  魏振华  徐蕾 《计算机应用》2015,35(4):1057-1061
针对人工蜂群(ABC)及其改进算法在求解高维复杂函数优化问题时,存在求解精度低、收敛速度慢、易陷入局部寻优且改进算法控制参数多的不足,提出一种分阶段搜索的改进人工蜂群算法。该算法设计了分阶段雇佣蜂搜索策略,使雇佣蜂在不同阶段具备不同的搜索特点,降低了算法陷入局部极值的概率;定义逃逸半径,使其能够更好地指导早熟个体跳出局部极值,避免了逃逸行为的盲目性;同时,采用均匀分布结合反向学习的初始化策略,促使初始解分布均匀且质量较优。通过对优化问题中8个典型高维复杂函数的仿真实验结果表明,该改进算法求解精度更高,收敛速度更快,更加适合高维复杂函数求解。  相似文献   

7.
为避免人工蜂群算法陷入早熟,提出一种基于动态搜索策略的人工蜂群算法,新算法改进了人工蜂群算法的搜索策略,将两种不同的搜索策略组合成新的搜索策略,以便动态利用两种不同搜索策略的优点,平衡了算法的局部搜索能力和全局搜索能力。基准函数的仿真实验表明,新算法收敛速度快、求解精度高、鲁棒性较强,适合求解高维复杂的全局优化问题。  相似文献   

8.
一种双种群差分蜂群算法   总被引:10,自引:0,他引:10  
人工蜂群算法(ABC)是一种基于蜜蜂群智能搜索行为的随机优化算法.为了有效改善人工蜂群算法的性能,结合差分进化算法,提出一种新的双种群差分蜂群算法(BDABC).该算法首先通过基于反向学习的策略初始化种群,使得初始化的个体尽可能均匀分布在搜索空间,然后将种群中的个体随机分成两组,每组采用不同的优化策略同时进行寻优,并通过在两群体之间引入交互学习的思想,来提高算法的收敛速度.基于6个标准测试函数的仿真实验表明,BDABC算法能有效避免早熟收敛,全局优化能力和收敛速率都有显著提高.  相似文献   

9.
为改善人工蜂群算法(ABC)的深度搜索能力,提出一种改进的人工蜂群算法(SABC)。借鉴混合蛙跳算法(SFLA)的进化机制,将蜂群划分为多个模因组,使每个新个体与自身所在模因组的最坏个体进行优劣比较,能够更加容易保存群体中的"新生"个体,改善群体的整体质量,增加算法的深度搜索能力。通过7个测试函数进行实验,统计结果表明了SABC算法在求解函数优化问题时具有较好的算法性能。  相似文献   

10.
The Journal of Supercomputing - Over the past few decades, there has been a surge of interest of using swarm intelligence (SI) in computer-aided optimization. SI algorithms have demonstrated their...  相似文献   

11.

Artificial bee colony algorithm simulates the foraging behavior of honey bees, which has shown good performance in many application problems and large-scale optimization problems. To model the bees foraging behavior more accurately, a food source-updating information-guided artificial bee colony algorithm is proposed in this paper. In this algorithm, some food source-updating information obtained during optimizing time is introduced to redefine the foraging strategies of artificial bees. The proposed algorithm has been tested on a set of test functions with dimension 30, 100, 1000 and compared with some recently proposed related algorithms. The experimental results show that the performance of artificial bee colony algorithm is significantly improved for both rotated problems and large-scale problems. Compared with the related algorithms, the proposed algorithm can achieve better or competitive performance on most test functions and greatly better performance on parts of test functions.

  相似文献   

12.
Computerized processes are supportive in the new age of medical treatment. Biomedical signals which are collected from the human body supply or important useful data that are related with the biological actions of human body organs. However, these signals may also contain some noise. Heart waves are commonly classified as biomedical signals and are non-stationary due to their statistical specifications. The probability distributions of the noise are very different, and for this reason there is no common method to remove the noise. In this study, adaptive filters are used for noise elimination and the transcranial Doppler signal is analyzed. The artificial bee colony algorithm was employed to design the adaptive IIR filters for noise elimination on the transcranial Doppler signal and the results were compared to those obtained by the methods based on popular and recently introduced evolutionary algorithms and conventional methods.  相似文献   

13.
14.
宁爱平  张雪英 《控制与决策》2013,28(10):1554-1558
利用随机过程理论,对人工蜂群算法收敛性进行理论分析,给出人工蜂群算法的一些数学定义和蜜源位置的一步转移概率,建立人工蜂群算法的Markov链模型,分析此Markov链的一些性质,论证了人工蜂群状态序列是有限齐次Markov链,且状态空间是不可约的。结合随机搜索算法的全局收敛准则,证明了人工蜂群算法能够满足随机搜索算法全局收敛的两个假设,保证算法的全局收敛。  相似文献   

15.
Neural Computing and Applications - Effective filter design plays an important role in signal processing applications. Multiple parameters must be considered to control the over-frequency response...  相似文献   

16.
Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers and practitioners. These algorithms have been used in the solution of various engineering problems. Recently, a relatively new swarm based optimization algorithm called the Artificial Bee Colony (ABC) algorithm has begun to attract interest from researchers to solve optimization problems. The aim of this study is to present an optimization algorithm based on the ABC algorithm for the discrete optimum design of truss structures. The ABC algorithm is a meta-heuristic optimization technique that mimics the process of food foraging of honeybees. Originally the ABC algorithm was developed for continuous function optimization problems. This paper describes the modifications made to the ABC algorithm in order to solve discrete optimization problems and to improve the algorithm’s performance. In order to demonstrate the effectiveness of the modified algorithm, four structural problems with up to 582 truss members and 29 design variables were solved and the results were compared with those obtained using other well-known meta-heuristic search techniques. The results demonstrate that the ABC algorithm is very effective and robust for the discrete optimization designs of truss structural problems.  相似文献   

17.
人工蜂群算法在重力坝断面优化设计中的应用   总被引:1,自引:0,他引:1       下载免费PDF全文
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。  相似文献   

18.
平面p-center问题是经典的NP难题,所以寻找高效的近似求解算法是解决实际应用问题时的基本需求。在人工蜂群算法的基础上,通过引入遗传算法的交叉和变异算子,改进局部解的搜索策略与搜索能力,即根据给定概率对当前解做交叉或变异运算,以获得更好的局部解,进而提出BeeGenP启发式求解算法,用于求解平面离散型p-center问题。通过构造测试数据,对所设计的算法进行了有效性验证,实验结果表明,BeeGenP算法与现有的M-ABC算法相比,算法的局部解搜索能力得到了提升,增加了搜索空间的多样性,在相同迭代次数约束下所得到的解的质量更高,而趋近收敛于最优解时的迭代次数则有较大幅度的降低。  相似文献   

19.
针对人工蜂群算法存在的易陷入局部最优、收敛速度慢的缺点,引入当前最优食物源和惯性权重函数,对该算法的食物源更新方式进行改进;针对支持向量回归机的参数优化问题,将其转化为组合优化问题,并使用改进的人工蜂群算法进行优化求解,进而得到人工蜂群算法优化SVR的预测模型。以短期交通流量数据为例,将该模型的预测结果与蚁群算法优化的支持向量回归机(ACO-SVR)、粒子群算法优化的支持向量回归机(PSO-SVR)和未改进的蜂群算法优化的支持向量回归机(ABC-SVR)进行对比分析,结果表明该模型的预测效果最优且运行时间最短,具有更好的学习能力和推广能力。  相似文献   

20.
This paper proposed a penalty guided artificial bee colony algorithm (ABC) to solve the reliability redundancy allocation problem (RAP). The redundancy allocation problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g. the total cost. RAP has been an active area of research for the past four decades. The difficulty that one is confronted with the RAP is the maintenance of feasibility with respect to three nonlinear constraints, namely, cost, weight and volume related constraints. In this paper nonlinearly mixed-integer reliability design problems are investigated where both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. The reliability design problems have been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. To the best of our knowledge the ABC algorithm can search over promising feasible and infeasible regions to find the feasible optimal/near-optimal solution effectively and efficiently; numerical examples indicate that the proposed approach performs well with the reliability redundant allocation design problems considered in this paper and computational results compare favorably with previously-developed algorithms in the literature.  相似文献   

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