Abstract
Bat algorithm (BA) is a nature-inspired metaheuristic algorithm which is widely used to solve the real world global optimization problem. BA is a population-based intelligent stochastic search technique that emerged from the echolocation features of bats and created from the mimics of bats foraging behavior. One of the major issue faced by the BA is frequently captured in local optima while handling the complex real-world problems. In this study, a new variant of BA named as improved bat algorithm (I-BAT) is proposed. Improved bat algorithm modifies the standard BA by enhancing its exploitation capabilities, and secondly for initialization of swarm, a quasi-random sequence Torus has been applied to overcome the issue of convergence and diversity. Population initialization is a vital factor in BA, which considerably influences the diversity and convergence of swarm. In order to improve the diversity and convergence, quasi-random sequences are more useful to initialize the population rather than the random distribution. The proposed strategy is applied to standard benchmark functions that are extensively used in the literature. The experimental results illustrate the superiority of the proposed technique. The simulation results verify the efficiency of proposed technique for swarm over the benchmark algorithm that is implemented for the function optimization.
Highlights
Optimization of the process that involves searching a vector from a function creates an optimum solution
We have proposed following one novel method of population initialization approach using low discrepancies sequence Torus named as (TO-Bat algorithm (BA)) that uses the torus quasi-random sequence to create the initialization of the swarm
To assure the robustness and integrity of proposed algorithms, a collection of nine benchmark test functions has been employed to perform the comparison of proposed improved bat algorithm (I-BAT) with Standard Bat algorithm
Summary
Optimization of the process that involves searching a vector from a function creates an optimum solution. Optimization algorithms are used to resolve the local and global search optimization issues. Optimization algorithms have two categories: stochastic algorithms and deterministic algorithm [1]. Deterministic algorithms use gradient and generate same solutions for all iterations, which are initiated with the same starting point. Stochastic algorithms generate distinct solutions even if the starting points are same and never uses gradient. Stochastic and population-based algorithms have two further parts: Heuristics and MetaHeuristics [3]. Swarm Intelligence (SI) is one of the natureinspired meta-heuristic algorithm that is frequently used solve the complex optimization problems. Some traditional neural networks and evolutionary algorithms [4] use for data classification and optimization
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 callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: 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.
