S. I: hybridization of neural computing with nature-inspired algorithms

Excerpt

Hybrid intelligent methods have proven their efficiency to solve NP-hard optimization problems. Nature-inspired method plays a significant role across domains to solve multi-criteria decision-making problems. With the development of many recent technologies such as deep learning, big data, and cloud computing, nature-inspired methods have become a subject of broad and current interest. Many recent key breakthroughs in science and engineering especially in improving computational ability of the systems are highly related to nature-inspired computing research. Over decades, multi-criteria decision-making problems are throwing new challenges to the researchers and scientist working in the field of intelligent computing. Several natures inspired algorithms have been proposed to solve these problems. In addition, the researchers have proposed hybridization of nature-inspired algorithms by mixing it with neural networks which not only solve the multi-criteria decision-making problems successfully but also, they significantly improve the overall system performance. This is evidenced by the rapidly growing number of research publications and by the availability of excessive related software systems. Now, the researchers have understood the necessity to develop hybrid algorithms combining neural networks with nature-inspired algorithm and integrate decision making into multi-criteria optimization, and the need for cross-fertilization between hybrid neural computing with nature-inspired algorithms and the multiple criteria decision-making communities has become apparent. …