An Automated Gradient Enhanced Bat Algorithm

Reddy, MP and Ganguli, R (2019) An Automated Gradient Enhanced Bat Algorithm. In: UNSPECIFIED, 18 November 2018 through 21 November 2018, pp. 2353-2360.

PDF Pro_iee_Sym_Ser_Com_Int_2353_2019.pdf - Published Version Restricted to Registered users only Download (195kB) | Request a copy

Abstract

Stochastic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and bat algorithm (BA) carry out global optimization but typically consume substantial computational effort. On the other hand, deterministic algorithms like gradient descent converge rapidly but may get stuck in the local minima of multimodal functions. Thus, a way that couples the strengths of stochastic and deterministic optimization algorithm schemes is needed for better accuracy of final solution without getting trapped in the local minima. Bat algorithm is a recently developed swarm optimization technique which has been found to be a powerful method for multimodal optimization problems. The standard BA shows premature convergence and reduced convergence speeds under some conditions. So, a recently proposed enhanced bat algorithm (EBA) is augmented with gradient search to create a gradient enhanced bat algorithm (GEBA). This paper presents GEBA for optimization which involves post-hybridization of EBA with a gradient based local search algorithm to ensure accurate local exploration at final stages of GEBA. GEBA is tested with several test functions and found to perform very well. An automated gradient enhanced bat algorithm (AGEBA) is developed to addresses the problem of selecting good initial guess for gradient based algorithm. AGEBA is found to be an efficient algorithm requiring only one tuning error parameter thereby saving considerable manual effort. © 2018 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	Copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Artificial intelligence; Genetic algorithms; Global optimization; Natural resources exploration; Newton-Raphson method; Optimization; Stochastic systems, Deterministic optimization; diversification; Gradient based algorithm; hybridization; Multimodal optimization problems; Quasi-Newton methods; stochastic; Stochastic optimization algorithm, Particle swarm optimization (PSO)
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	15 Apr 2019 05:19
Last Modified:	15 Apr 2019 05:19
URI:	http://eprints.iisc.ac.in/id/eprint/62093

Actions (login required)

View Item