An Intelligent Hybridization of ABC and LM Algorithms With Constraint Engineering Applications

Abstract

Artificial Bee Colony (ABC) and Levenberg-Marquardt (LM) optimization algorithms are applied efficiently for nonlinear constrained and unconstrained optimization problems in literature. In this paper, an intelligent hybridization method of the ABC and LM algorithms is proposed such that their global and local exploitation superiorities are unified to reduce the computational time and escape from local minima of optimization problem. In order to prove the capability of proposed hybrid algorithm, twofold experiment is conducted. In the first phase, the hybrid algorithm is applied to optimize several nonlinear unimodal, multimodal and shifted benchmark functions. Secondly, it is applied to the constrained engineering problems and compared to literature works in several performance criteria. © 2017 Elsevier Inc. All rights reserved.