Abstract
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.
Highlights
Optimization problems are usually encountered in a mount of real-word areas such as artificial intelligence, computer science, pattern recognition, information theory, etc
QMBA is combined with Gaussian probability distribution, which can improve the diversity, magnify the search range, and avoid falling into local optimum
Gaussian QMBA (GQMBA) inherits the characteristics of the original bat algorithm (BA) and QMBA, including simplicity, feasibility, and ease to implement
Summary
Optimization problems are usually encountered in a mount of real-word areas such as artificial intelligence, computer science, pattern recognition, information theory, etc. Due to its low population diversification, it may get trapped in local optima and premature convergence when solving high-dimensional optimization problems [20]. All stochastic coefficients in the QMBA algorithm are generated using the uniform probability distribution, which can only provide a relatively small search range. Erefore, QMBA still faces a certain degree of premature convergence To solve this issue, this article presents a technique of quantum-behaved BA directed by mean best position (QMBA) based on Gaussian distribution (GQMBA) for numerical function optimization. Where β is a uniform random number in the range of [0, 1], fqumeinncayn, drefspmeacxtiivnedlyic,agtbet the minimum and means the current maximum global best fresolution With these equations, the global search capacity of BA can be guaranteed. E basic procedure of BA is described as the pseudocode illustrated in Algorithm 1
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
