Mapping of Real World Problems to Nature Inspired Algorithm using Goal based Classification and TRIZ

TL;DR

This paper introduces a novel framework that uses goal-based classification and TRIZ to map real-world problems to suitable Nature Inspired Algorithms, enhancing problem-solving efficiency.

Contribution

It proposes a new classification of Nature Inspired Algorithms based on their end goals and integrates TRIZ for effective problem-to-algorithm mapping.

Findings

Framework successfully maps real-world problems to appropriate NIA.

Goal-based classification improves selection accuracy of algorithms.

Application examples demonstrate practical utility of the approach.

Abstract

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.