A holistic review on artificial intelligence techniques for well placement optimization problem Abstract   Well placement optimization is one of the major challenging factors in the field development process of oil and gas industry. The objective function of well placement optimization is considered as high dimensional, discontinuous and multimodel. Over the last decade, both gradientbased and gradientfree optimization methods have been implemented to tackle this problem. Natureinspired gradientfree optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. These optimization techniques are implemented as standalone or as hybrid form to maximize the economic factors. In this paper, several natureinspired metaheuristic optimization techniques and their application to maximize the economic factors are reviewed. Newly developed optimization algorithms are very efficient and favorable when compared to other established optimization algorithms and in all cases, it has been noticed that hybridization of two or more algorithms works better than standalone algorithms. Furthermore, none of the single optimization techniques can be established as being universally superior which aligns with no free lunch theorem. For future endeavor, combining optimization methods and exploiting multiple optimization processes for faster convergence and developing efficient proxy model is expected.

Cat Swarm Optimization Algorithm  A Survey and Performance Evaluation This paper presents an indepth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarmbased optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.