[1]LIU Xiaofang,LIU Peizhong,LUO Yanmin,et al.Improved artificial bee colony algorithm based on enhanced local search[J].CAAI Transactions on Intelligent Systems,2017,12(5):684-693.[doi:10.11992/tis.201612026]
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Improved artificial bee colony algorithm based on enhanced local search

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 5 Page number: 684-693 Column: 学术论文—机器学习 Public date: 2017-10-25
Title:
Improved artificial bee colony algorithm based on enhanced local search
Author(s):
LIU Xiaofang1;  LIU Peizhong1;  LUO Yanmin2;  FAN Yuling1
1. Engineering school, Huaqiao University, Quanzhou 362021, China;
2. School of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Keywords:
artificial bee colony algorithm; high-dimension chaotic system; fitness evaluation; search tactics; optimization algorithm; evolutionary algorithm; convergence analysis; accuracy analysis; intelligent algorithm
CLC:
TP18
DOI:
10.11992/tis.201612026
Abstract:
The shortcomings of the artificial bee colony algorithm (ABC) are its uneven initial population distribution and weak local search. In this paper, we propose an ABC algorithm based on enhanced local search (ESABC). First, we employ a high-dimension chaotic system (Lorenz system) to obtain the ergodic and regular initial populations and to avoid the blindness of random initialization in the population initialization stage. Then, we introduce improved fitness evaluation methods based on the logarithmic function to increase the differences between individuals, reduce selection pressure, and avoid premature convergence. Lastly, inspired by the differential evolution algorithm, we propose a new search tactic that uses the best individual in the contemporary population to guide the renewal of the next generation, and thereby enhance the local search ability. We examined the performance of the proposed approach with 12 classic testing functions and compared the results with the basic and other ABCs. As documented in the experimental results, the proposed algorithm exhibits good optimization performance and can improve both the accuracy and convergence speed of the algorithm.
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