Abstract
The beetle antennae search algorithm (BAS) is a new heuristic algorithm proposed in 2017. BAS is a simulation optimization algorithm based on beetle antennae, which determine the direction of flight by sensing the smell of food and thereby find the food. The BAS algorithm has been proven to have better optimization speed and precision when searching for solutions to low-dimensional problems. However, when solving high-dimensional problems, the algorithm easily falls into a local optimum. To improve the search ability of BAS, this paper introduces inertia weight, which leads the algorithm to perform a global search in early stages and a local search in later stages, which greatly improves the optimization precision of the algorithm. Economic load distribution (ELD) is a typical optimization problem in power systems. This paper analyses the problem of ELD and its mathematical model and then uses a 3-machine 6-node example to apply the improved BAS algorithm to ELD. Finally, the improved BAS algorithm is compared with PSO and other algorithms on the basis of the optimization results of test functions. The conclusion is drawn that the improved BAS algorithm has advantages in dealing with ELD problems.
Highlights
The Economic load distribution (ELD) problem refers to optimizing the plan for starting and shutting down a unit during a certain operating period
This paper proposes a new beetle antennae search algorithm based on linearly decreasing inertia weight, which enhances the individual’s global search and local search ability, so that the algorithm can jump out of the local optimal value, and the stability of the algorithm is improved
To improve the performance of the beetle antennae search algorithm (BAS) algorithm, a new method named WSBAS has been proposed in this paper
Summary
The ELD problem refers to optimizing the plan for starting and shutting down a unit during a certain operating period. M. Lin et al.: Improved BAS Algorithm and Its Application on ELD evolution operator has been proposed. To expand its application scope, Jiang and Li [18] improved the basic BAS algorithm and applied it to a multi-objective optimization problem successfully. The learning ability of individuals in [18] was still limited, and the algorithm fell into a local optimum To solve this problem, this paper proposes a new beetle antennae search algorithm based on linearly decreasing inertia weight, which enhances the individual’s global search and local search ability, so that the algorithm can jump out of the local optimal value, and the stability of the algorithm is improved
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
