## 1 Introduction
These methods are generated by taking into consideration the type of the problem (i.e., binary, discrete, continuous, perpetration, or structured). In the second generation of EAs, only one optimization template is available for almost all optimization problems. These includes Differential Evolution (DE) [13], Particle Swarm Optimization (PSO) [14]. Nowadays, a plethora of EAs is mostly inspired by natural phenomena related to human behavior, physical wisdom, animal swarm survival strategies, and chemical principles. These methods will be surveyed in the literature review section. The common features of these methods can be summarized as follows: (i) they are population-based, (ii) they have an iterative improvement process, (iii) They turned toward the optimal solution through the special operator(s), (iv) they can explore several search space niches and exploit each niche, (v) they embed the problem-specific knowledge mapping the phenotype to genotype, and (vi) they can provide a suitable balance between diversification and intensification of the problem search space. The main difference between them is their movement way through the search space via their operators where their definitions of the search space niches are varied. Due to the complex nature of the optimization problem, where there is up-to-now a superior EA can ultimately tackle all optimization problems and excel over all other EAs. This is stated in a pioneer theorem in optimization named No Free Lunch (NFL) where there is no single algorithm that performs better than others in every case or even for the same problem in different instances [15]. Therefore, NFL opens the door for new innovation theories to stem other natural phenomena and propose other intelligent EAs with the ability to efficiently tackle optimization problems with rugged and huge search space. Although there are many studies in the optimization field, there are still various behaviors in nature that have not been studied yet [16; 17]. One of these behaviors is the behavior of lemurs animal in the movement to search for food or escape from other predators. Lemurs can only be found in Madagascar and the neighboring Comoro Islands, which are located off the coast of Mozambique, Africa. In this paper, the Lemurs Optimizer (LO) is proposed as an evolutionary algorithm. LO is stemmed by its behavior in locomotor behavior: leap up and dance-hup. The LO behavior is formalized in terms of optimization context. LO is evaluated using twenty-three standard optimization functions circulated well in the literature. In addition, some real engineering problems are also used. Initially, the effect of some parameters in LO is studied to decide which is the best configuration. Thereafter, the comparative evaluation is conducted against six well-established methods. The results prove the superiority of LO over other comparative algorithms. For statistical evaluation, the Wilcoxon Mann-Whitney test shows the significance of LO results. In conclusion, LO is a new optimization algorithm that can be applied to a large variety of global optimization problems efficiently. This paper's remaining subsections are organized as follows: The literature review of the previous natural inspired evolutionary algorithms is summarized in Section 2. The inspiration and procedural steps of the LO algorithm are proposed and described in Section 3. The evaluation of the proposed LO is conducted and the experimental resultsare analyzed and compared in Section 4. Finally, in Section 5, the conclusion is presented, as well as various scenarios for future development.