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

• URL: http://arxiv.org/abs/2010.03795v1
• Date: Thu, 8 Oct 2020 06:55:31 GMT
• Title: Mapping of Real World Problems to Nature Inspired Algorithm using Goal based Classification and TRIZ
• Authors: Palak Sukharamwala and Manojkumar Parmar
• Abstract summary: A novel method based on TRIZ to map the real-world problems to nature problems is explained here. For this framework to work, a novel classification of NIA based on the end goal that nature is trying to achieve is devised.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• EHOP: A Dataset of Everyday NP-Hard Optimization Problems [66.41749917354159]
  Everyday Hard Optimization Problems (EHOP) is a collection of NP-hard optimization problems expressed in natural language. EHOP includes problem formulations that could be found in computer science textbooks, versions that are dressed up as problems that could arise in real life, and variants of well-known problems with inverted rules. We find that state-of-the-art LLMs, across multiple prompting strategies, systematically solve textbook problems more accurately than their real-life and inverted counterparts.
  arXiv  Detail & Related papers  (2025-02-19T14:39:59Z)
• Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization [56.17811386955609]
  Graph-structured challenges are inherently difficult due to their nonlinear and intricate nature. In this study, we propose transforming graphs into images to preserve their higher-order structural features accurately. By combining the innovative paradigm powered by multimodal large language models with simple search techniques, we aim to develop a novel and effective framework.
  arXiv  Detail & Related papers  (2025-01-21T08:28:10Z)
• Unraveling the Versatility and Impact of Multi-Objective Optimization: Algorithms, Applications, and Trends for Solving Complex Real-World Problems [4.023511716339818]
  Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years. This paper examines recently developed MOO-based algorithms. In real-world case studies, MOO algorithms address complicated decision-making challenges.
  arXiv  Detail & Related papers  (2024-06-29T15:19:46Z)
• Introduction to Algogens [0.0]
  Algogens is a promising integration of generative AI with traditional algorithms. The book explores the basics of Algogens, their development, applications, and advantages. It offers a balanced look at the prospects and obstacles facing Algogens.
  arXiv  Detail & Related papers  (2024-03-03T07:52:10Z)
• Route Planning Using Nature-Inspired Algorithms [0.0]
  There are many different algorithms for solving optimization problems that are commonly described as Nature-Inspired Algorithms (NIAs) We will first give an overview of Nature-Inspired Algorithms, followed by their classification and common examples. We will then discuss how the NIAs have applied to solve the route planning problem.
  arXiv  Detail & Related papers  (2023-07-22T17:37:43Z)
• Multi-Objective Policy Gradients with Topological Constraints [108.10241442630289]
  We present a new algorithm for a policy gradient in TMDPs by a simple extension of the proximal policy optimization (PPO) algorithm. We demonstrate this on a real-world multiple-objective navigation problem with an arbitrary ordering of objectives both in simulation and on a real robot.
  arXiv  Detail & Related papers  (2022-09-15T07:22:58Z)
• Learning Iterative Reasoning through Energy Minimization [77.33859525900334]
  We present a new framework for iterative reasoning with neural networks. We train a neural network to parameterize an energy landscape over all outputs. We implement each step of the iterative reasoning as an energy minimization step to find a minimal energy solution.
  arXiv  Detail & Related papers  (2022-06-30T17:44:20Z)
• Nature-Inspired Optimization Algorithms: Research Direction and Survey [0.0]
  Nature-inspired algorithms are commonly used for solving the various optimization problems. We classify nature-inspired algorithms as natural evolution based, swarm intelligence based, biological based, science based and others. The purpose of this review is to present an exhaustive analysis of various nature-inspired algorithms based on its source of inspiration, basic operators, control parameters, features, variants and area of application where these algorithms have been successfully applied.
  arXiv  Detail & Related papers  (2021-02-08T06:03:36Z)
• Constraint Programming Algorithms for Route Planning Exploiting Geometrical Information [91.3755431537592]
  We present an overview of our current research activities concerning the development of new algorithms for route planning problems. The research so far has focused in particular on the Euclidean Traveling Salesperson Problem (Euclidean TSP) The aim is to exploit the results obtained also to other problems of the same category, such as the Euclidean Vehicle Problem (Euclidean VRP), in the future.
  arXiv  Detail & Related papers  (2020-09-22T00:51:45Z)
• Differentiable Causal Discovery from Interventional Data [141.41931444927184]
  We propose a theoretically-grounded method based on neural networks that can leverage interventional data. We show that our approach compares favorably to the state of the art in a variety of settings.
  arXiv  Detail & Related papers  (2020-07-03T15:19:17Z)
• Nature-Inspired Optimization Algorithms: Challenges and Open Problems [3.7692411550925673]
  Problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness.
  arXiv  Detail & Related papers  (2020-03-08T13:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.