## 1 Introduction  
In the past 50 years, artificial intelligence (AI) has been centered on developing computers and algorithms that focus on human cognitive abilities. This type of AI, which is also called mainstream AI, has proven to be very successful to solve problems that are computationally heavy and lack human efficiency, such as controlling power plant systems or aircraft dynamics. In the mid-1980s, there was a surge to explore a new type of AI inspired by biological intelligence. It is called bio-inspired AI, the 21 st century artificial intelligence (Figure 1). Accordingly, the bio-inspired AI can be categorized into seven sub-categories, which are evolutionary systems, cellular systems, neural systems, development systems, immune systems, behavioral systems, and collective systems [1]. One form of bio-inspired AI which has been emerging rapidly is swarm intelligence (SI). SI is inspired by studying the behavior of groups, or swarms, of a biological organism. Social insects such as ants, bees, and termites live in colonies or swarms, and every single member in the colony seems to have its purpose yet the colony (swarm) appears organized [2]. The most unique features of such swarms are the fact that they portray complex collective behavior despite the simplicity of the individuals (agents) that make up the swarm. The common complex collective behaviors are aggregation [3], foraging [4], flocking [5], cooperation [6], and stigmergy [7]. Models of this system have been proven to solve a difficult and complex real-world problem such as optimization [8; 9], and target search [10; 11]. The main representatives of SI approaches are particle swarm optimization (PSO) [12], bees algorithm (BA) [13], artificial bee colony optimization (ABC) [14], ant colony optimization (ACO) [15; 16], bacterial foraging optimization (BFO) [4], glow-worm swarm optimization (GSO) [17], and firefly algorithm (FA) [18]. SI has also been actively incorporated with the latest robotic technology by researchers and technologists, which has emerged as the swarm robotics (SR) domain. SR, together with the implementation of SI methods, represents the computational agent into physically implemented or simulated robotic devices. SR strategies have continuously increased the attention in many applications, especially those that are 3D (dangerous, dirty, and dull) mission-related, such as search and rescue [19], pollution detection [20], and natural disaster monitoring [21]. These types of applications require a large number of agents, are time consuming and may even be dangerous to a human being [22]. In addition, all the given applications have a common important task, which is to cooperatively search for targets in unknown environments. Compared to a single robot, swarm robots can significantly improve efficiency and provide better robustness and adaptability in target searching tasks [23]. The problem of target searching has continued for a very long time, and more civilian applications have emerged. This includes a wide variety of high-impact applications; for example, rescue operations in disaster areas, exploration for natural resources, environmental monitoring, and air surveillance. By adapting the SR strategies, swarm robots can sense the environment, estimate the target's distance, and cooperatively coordinate swarm movement by taking advantage of this information. The ultimate goal is to search for targets within the minimum time while avoiding collisions with other robots [11]. Therefore, the purpose of this paper is to deliver a systematic literature review (SLR) covering the main published solutions of target search problems using SR strategies. This paper expands the algorithm behind the SR strategies and their significant result, challenges, and future dimensions towards the target search problem.