Probabilistic Optimization Algorithms for numerical function optimization problems

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

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An enhanced artificial bee colony algorithm for numerical function optimization

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/806/1/012024 ◽

2020 ◽

Vol 806 ◽

pp. 012024

Author(s):

Yi Yu ◽

Yonggang Wu ◽

Xinglong Liu

Keyword(s):

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Function Optimization ◽

Numerical Function ◽

Bee Colony ◽

Numerical Function Optimization

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Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

Computational Intelligence and Neuroscience ◽

10.1155/2019/5652340 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Xingwang Huang ◽

Chaopeng Li ◽

Yunming Pu ◽

Bingyan He

Keyword(s):

Probability Distribution ◽

Bat Algorithm ◽

Random Coefficients ◽

Function Optimization ◽

Premature Convergence ◽

Local Optimum ◽

Search Range ◽

Numerical Function ◽

Numerical Function Optimization ◽

Gaussian Probability Distribution

Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.

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