INTRODUCTION

The standard bat algorithm (BA) is inspired by the echolocation characteristics of microbats. This demo solves a function of d=10 dimensions. It is straightforward to extend it to solve other functions and optimization problems.

Nature-inspired algorithms have become a very promising alternative for solving very hard optimization problems in sciences and engineering. In the last two decades, many natureinspired algorithms have been developed. The inspirations for developing such nature-inspired algorithms often come from biological, chemical and physical processes in nature. In addition, some algorithms were developed by drawing characteristics that based on sociology, history or even sports. A brief review and taxonomy were proposed in the paper by Fister et al. According to the current literature, some popular nature-inspired algorithms are as follows:

Ant colony optimization (ACO), based on ant foraging behaviour.
Artificial bee colony (ABC) , based on the behaviour of honey bees.
Cuckoo search (CS) , based on the brooding behaviour of cuckoo species.
Firefly algorithm (FA) , inspired by the flashing behaviour of tropical fireflies.
Particle swarm optimization (PSO) , based on the flocking behavior of birds.
and many evolutionary algorithms

However, this short list of algorithms is just a tip of the algorithm iceberg, because there are more than 100 different algorithms in the literature. Therefore, it is not possible to cover even a fraction of these algorithms in one paper. Therefore, this paper is devoted to the bat algorithm (BA) which belongs to swarm intelligence.

BA was developed in 2010 and significant progress has been made in the last 4 years. The aim of this paper is to review the bat algorithm and its recent developments, with an emphasis on the recent publications in 2013 and 2014 . We also discuss the latest improvements and applications concerning the bat algorithm.

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THE BRIEF HISTORY OF THE BAT ALGORITHM

Since the appearance of the original paper on the bat algorithm, the literature started to expand with a wide range of applications. The original paper outlined the main formulation of the algorithm and applied the bat algorithm to study function optimization with promising results. In fact, studies indicated that BA can perform better than genetic algorithms and particle swarm optimization.

Then, Yang extended the BA to solve multi-objective optimization [31]. In addition, Yang and Gandomi applied the BA to engineering optimization with extensive results Probably the first hybrid variant of the bat algorithm was introduced by Wang et al and Fister proposed a hybrid bat algorithm. Furthermore, discrete bat algorithms and parallelization versions also appeared.

RECENT VARIANTS OF THE BAT ALGORITHM

There are many new variants of the bat algorithm in the recent literature. In this brief paper, we summarize some the latest variants in Table I. For example, Mallikarjuna et al. proposed a binary bat algorithm for solving the wellknown economic load dispatch problem with the valve-point effect, and they concluded that their binary bat algorithm has many advantages. One of the biggest advantages is that BA can provide very quick convergence at the initial stage and can automatically switch from exploration to exploitation when the optimality is approaching. In addition, Sabba and Chikhi proposed the so-called discrete binary bat algorithm by using the sigmoid function. In their paper , a new variant of BA called CBA (chaotic bat algorithm) by using chaotic maps to replace the uniform distribution used in the standard bat algorithm.