A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

2010 ◽  
Vol 20 (01) ◽  
pp. 39-50 ◽  
Author(s):  
HAI-BIN DUAN ◽  
CHUN-FANG XU ◽  
ZHI-HUI XING
Keyword(s):  
Evolutionary Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Experimental Comparison ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Comparison Results ◽  
Bee Colony Optimization ◽  
Continuous Optimization Problems

In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

scholarly journals A Modified Artificial Bee Colony Algorithm with Firefly Algorithm Strategy for Continuous Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Amnat Panniem ◽  
Pikul Puphasuk
Keyword(s):  
Firefly Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Control Parameters ◽  
Exploration And Exploitation ◽  
Bee Colony ◽  
Continuous Problems ◽  
Local Solutions ◽  
Continuous Optimization Problems

Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.

scholarly journals Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Lianbo Ma ◽  
Kunyuan Hu ◽  
Yunlong Zhu ◽  
Ben Niu ◽  
Hanning Chen ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Complete Solution ◽  
Continuous Optimization ◽  
Benchmark Problems ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Discrete And Continuous Optimization ◽  
Upper Level ◽  
Abc Method

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

Sign in / Sign up

Export Citation Format

Share Document