897 Copyright © 2017, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 39 DOI: 10.4018/978-1-5225-2229-4.ch039 ABSTRACT There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a diﬀerent shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classiﬁcation accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classiﬁcation accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classiﬁcation accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classiﬁcation accuracy and the processing time. Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters Ahmed.T. Sahlol Damietta University, Egypt & Scientiﬁc Research Group in Egypt (SRGE), Egypt Aboul Ella Hassanien Cairo University, Egypt & Scientiﬁc Research Group in Egypt (SRGE), Egypt