Mapping of Real World Problems to Nature Inspired Algorithm using Goal based Classification and TRIZ
This addresses the problem of algorithm selection for researchers and practitioners in optimization and computational fields, though it appears incremental as it builds on existing TRIZ and NIA concepts.
The paper tackles the challenge of selecting appropriate Nature Inspired Algorithms (NIA) for real-world problems by proposing a novel framework that uses TRIZ and goal-based classification to map problems to nature-inspired solutions, enabling identification of the best NIA.
The technologies and algorithms are growing at an exponential rate. The technologies are capable enough to solve technically challenging and complex problems which seemed impossible task. However, the trending methods and approaches are facing multiple challenges on various fronts of data, algorithms, software, computational complexities, and energy efficiencies. Nature also faces similar challenges. Nature has solved those challenges and formulation of those are available as Nature Inspired Algorithms (NIA), which are derived based on the study of nature. A novel method based on TRIZ to map the real-world problems to nature problems is explained here.TRIZ is a Theory of inventive problem solving. Using the proposed framework, best NIA can be identified to solve the real-world problems. For this framework to work, a novel classification of NIA based on the end goal that nature is trying to achieve is devised. The application of the this framework along with examples is also discussed.