# A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
## 1 Introduction  
Over the past twenty years, the heuristic optimization algorithms have been widely appreciated for their simplicity, flexibility, and robustness. Common heuristic optimization algorithms include genetic algorithm (GA) [1], simulated annealing [2], crew search algorithm [3], ant colony optimization [4], differential evolution (DE) [5], particle swarm optimization (PSO) [6], bat algorithm (BA) [7], cuckoo search algorithm (CSA) [8], whale optimization algorithm (WOA) [9], firefly algorithm (FA) [10], grey wolf optimizer (GWO) [11], teaching-learning-based optimization [12], artificial bee colony (ABC) [13], and chimp optimization algorithm (ChOA) [14]. As technology continues to evolve and update, the heuristic optimization algorithms are currently widely applied in various areas of real life, such as the welded beam problem [15], feature selection [16; 17; 18], the welding shop scheduling problem [19], economic dispatch problem [20], training neural networks [21], path planning [15; 22], chum prediction [23], image segmentation [24], 3D reconstruction of porous media [25], bankruptcy prediction [26], tuning of fuzzy control systems [27; 28; 29], interconnected multi-machine power system stabilizer [30], power systems [31; 32], large scale unit commitment problem [33], combined economic and emission dispatch problem [34], multi-robot exploration [35], training multi-layer perceptron [36], parameter estimation of photovoltaic cells [37], and resource allocation in wireless networks [38]. Although there are many metaheuristic algorithms, each has its shortcomings. GWO does not have a good local and global search balance. In [39; 40], the authors studied the possibility of enhancing the exploration process in GWO by changing the original controlparameters. GWO lacks population diversity, and in [41], the authors replaced the typical real-valued encoding method with a complex-valued one, which increases the diversity of the population. ABC has the disadvantages of slow convergence and lack of population diversity. In [42], the authors' proposed approach refers to the procedure of differential mutation in DE and produces uniform distributed food sources in the employed bee phase to avoid the local optimal solution. In [43], in order to speed up the convergence of ABC, the authors proposed a new chaos-based operator and a new neighbour selection strategy improving the standard ABC. WOA has the disadvantage of premature convergence and easily falls into the local optimum. In [37], the authors modified WOA using chaotic maps to prevent the population from falling into local optima. In [44] WOA was hybridized with DE, which has a good exploration ability for function optimization problems to provide a promising candidate solution. CSA faces the problem of getting stuck in local minima. In [45], the authors addressed this problem by introducing a new concept of time varying flight length in CSA. BA has many challenges, such as a lack of population diversity, insufficient local search ability, and poor performance on high-dimensional optimization problems. In [46], Boltzmann selection and a monitor mechanism were employed to keep the suitable balance between exploration and exploitation ability. FA has a few drawbacks, such as computational complexity and convergence speed. In order to overcome such obstacles, in [47], the chaotic form of two algorithms, namely the sine-cosine algorithm and the firefly algorithms, are integrated to improve the convergence speed and efficiency, thus minimizing several complexity issues. DE is an excellent algorithm for dealing with nonlinear and complex problems, but the convergence rate is slow. In [48], the authors proposed that oppositional-based DE employs OBL to help population initialization and generational jumping. There are many ways to help improve the performance of the metaheuristic algorithm, for example, Opposition-Based Learning (OBL) [49], chaotic [50], Levy flight [51], etc. [PERSON] introduced OBL [49] in 2005.