Computational Intelligence for Modeling and Optimization of RFEH and WPT Systems: A Comprehensive Survey: Computational Intelligence for RFEH and WPT Systems

Several practical engineering optimization problems are computationally demanding, requiring a large amount of computer time, processing power, and memory. These challenges can be mitigated by human-engineered systems exhibiting intelligent behavior. With the evolution of high-speed digital computers, the use of computational intelligence (CI) techniques has increased rapidly. According to Bezdek [1] , “A system is called computationally intelligent if it deals with low-level data such as numerical data, has a pattern-recognition component and does not use knowledge in the artificial intelligence (AI) sense, and additionally when it begins to exhibit computational adaptivity, fault tolerance, speed approaching human-like turnaround and error rates that approximate human performance.” Another definition, by Engelbrecht [2] , states that “CI is the study of adaptive mechanisms that enable or facilitate intelligent behavior in complex and changing environments. These mechanisms include those Artificial Intelligence paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate.” Thus, CI is the general term used to classify all such nature-inspired methodologies and their associated theories and applications. The five important paradigms of the CI technique are artificial neural networks (ANNs), swarm intelligence (SI), evolutionary computation (EC), and fuzzy systems (FSs). The origin of each technique can be connected to a natural system; for example, an ANN imitates the biological neural system. SI models the behavior of organisms living in swarms, whereas EC models the natural evolution system. Similarly, an FS originates from human thinking processes. Many of the problems involved in designing next-generation systems can be resolved using these CI techniques or their combinations.