Abstract
The bat algorithm (BA) is a recent heuristic optimization algorithm based on the echolocation behavior of bats. However, the bat algorithm tends to fall into local optima and its optimization results are unstable because of its low global exploration ability. To solve these problems, a novel bat algorithm based on an integration strategy (IBA) is proposed in this paper. Through the integration strategy, an appropriate operator is adaptively selected to perform global search, so that the global search ability of the IBA is improved. Furthermore, the IBA disturbs the local optimum through a linear combination of Gaussian functions with different variances to avoid becoming trapped in local optima. The IBA also updates the velocity equation with an adaptive weight to further balance the exploration and exploitation. Moreover, the global convergence of the IBA is proved based on the convergence criterion of a stochastic algorithm. The performance of the IBA is evaluated on CEC2013 benchmark functions and compared with that of the standard BA as well as several of its variants. The results show that the IBA is superior to other algorithms.
Highlights
Optimization usually involves highly nonlinear complex problems with many design variables and complex constraints [1]
Harmony search is an algorithm inspired by the music composition process of musicians. e particle swarm optimization (PSO) algorithm [4] is inspired from swarming behaviors such as bird flocking and fish schooling in nature
We present three main contributions: (i) we adaptively select the appropriate operator for performing global search through the integration strategy, which can improve the global search ability of the algorithm; (ii) we disturb a local optimum through a linear combination of Gaussian functions with different variances, so that the IBA has the ability to jump out of the local optima; and (iii) we update the velocity equation of the standard bat algorithm (BA) with an adaptive weight to balance the exploration and exploitation and keep the algorithm stable
Summary
Optimization usually involves highly nonlinear complex problems with many design variables and complex constraints [1]. We present three main contributions: (i) we adaptively select the appropriate operator for performing global search through the integration strategy, which can improve the global search ability of the algorithm; (ii) we disturb a local optimum through a linear combination of Gaussian functions with different variances, so that the IBA has the ability to jump out of the local optima; and (iii) we update the velocity equation of the standard BA with an adaptive weight to balance the exploration and exploitation and keep the algorithm stable. Is paper presents the following improvements: (i) an adaptive weight; (ii) a representation of the random disturbance using a linear combination of two Gaussian distributions with different variances; (iii) determination of optimal solution with an integrated strategy; and (iv) local search.
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
