**1. Introduction**

The rapid growth of the size and complexity of optimization problems implies that the traditional optimization algorithms are becoming more uncertain for solving these problems [1]. Metaheuristic algorithms [2–4] have proved to be a viable solution to this challenge. Inspired by nature, these strong metaheuristic algorithms are applied to solve NP-hard problems, such as flow shop scheduling [5–9], image encryption [10–12], feature selection [13–15], facial feature detection [16,17], path planning [18,19], cyber-physical social systems [20,21], texture discrimination [22], factor evaluation [23], saliency detection [24], classification [25], engineering optimization [26], object extraction [27], gesture segmentation [28], economic load dispatch [29], shape design [30], big data and large-scale optimization [31], signal processing [32], multi-objective and many-objective optimization [33–35], unit commitment [36], vehicle routing [37,38], and the knapsack problem [39,40]. Some of the well-known methods in this area are genetic algorithms (GAs) [41], particle swarm optimization (PSO) [42–45], differential evolution (DE) [19,46,47], monarch butterfly optimization (MBO) [48–52], artificial bee colonies (ABCs) [53], earthworm optimization algorithms (EWAs) [54], ant colony optimization (ACO) [55], cuckoo search (CS) [56–62], krill herd (KH) [63–67], firefly algorithms (FAs) [68–73], simulated annealing (SA) [74], intelligent water drop (IWD) [75], water cycle algorithms (WCAs) [76], moth search (MS) [77], monkey algorithms (MAs) [78], evolutionary strategy (ES) [79], free search (FS) [80], probability-based incremental learning (PBIL) [81], biogeography-based optimization (BBO) [82–85], dragonfly algorithms (DAs) [86], interior search algorithms (ISAs) [87], brain storm optimization (BSO) [88,89], bat algorithms (BAs) [18,90–97], stud GAs (SGAs) [98], harmony search (HS) [99–102], fireworks algorithms (FWAs) [103], and chicken swarm optimization (CSO) [104].

Based on the herding behavior of elephants, a new swarm intelligence-based global optimization algorithm, namely elephant herding optimization (EHO), was proposed byWang et al. [105]. Two special operators, a clan updating operator and a separating operator, are included in EHO. The elephants in each clan are updated with respect to their current position and the position of the matriarch. The acceptable performance of EHO has drawn much attention from scholars and engineers. In this paper, a comprehensive review for the EHO-based algorithms and their applications are presented. The remainder of this paper is organized as follows. The main steps of the EHO is detailed in Section 2. Improved EHO algorithm variants are presented in Section 3. Section 4 describes the EHO applications for solving engineering optimization problems. Finally, Section 5 presents a conclusion and suggestions for future work.

#### **2. Historical Development of Elephant Herding Optimization**

#### *2.1. Elephant Herding Optimization Research Studies*

The EHO algorithm with the herding behavior of elephant groups has received significant attention from scholars [105]. Ninety-three related studies have been published in journals/dissertations/conferences up to 23 April 2020 (Figure 1) since EHO was proposed in 2015. Among these 93 papers, 2 papers were published in 2015 and 2016, 14 papers were published in 2017, 21 papers were published in 2018, and 32 and 24 papers were published in 2019 and 2020, respectively.

**Figure 1.** Related (Elephant Herding Optimization) EHO publications since 2015.

#### *2.2. Basics of Elephant Herding Optimization*

Elephants, as social creatures, live in social structures of females and calves. An elephant clan is headed by a matriarch and composed of a number of elephants. Female members like to live with family members, while the male members tend to live elsewhere. They will gradually become independent of their families until they leave their families completely. The population of all elephants is shown in Figure 2. The EHO technique proposed by Wang et al. in 2015 [105] was developed after studying natural elephant herding behavior. The following assumptions are considered in EHO.

**Figure 2.** Population of elephants.

(1) Some clans with fixed numbers of elephants comprise the elephant population.

(2) A fixed number of male elephants will leave their family group and live solitarily far away from the main elephant group in each generation.

(3) A matriarch leads the elephants in each clan.
