Nature-Inspired Computing Paradigms in Systems

2021 ◽  
Keyword(s):  
Nature Inspired Computing

An efficient hexagonal image framework using pseudo hexagonal pixel for computer vision applications

2021 ◽  
pp. 1-14
Author(s):  
Prathibha Varghese ◽  
G. Arockia Selva Saroja
Keyword(s):  
Computer Vision ◽  
Heuristic Algorithms ◽  
Ganglion Cells ◽  
Signal To Noise Ratio ◽  
Hexagonal Lattice ◽  
Display Device ◽  
Angular Correction ◽  
Nature Inspired Computing ◽  
Computer Vision Applications ◽  
Simulated Images

Nature-inspired computing has been a real source of motivation for the development of many meta-heuristic algorithms. The biological optic system can be patterned as a cascade of sub-filters from the photoreceptors over the ganglion cells in the fovea to some simple cells in the visual cortex. This spark has inspired many researchers to examine the biological retina in order to learn more about information processing capabilities. The photoreceptor cones and rods in the human fovea resemble hexagon more than a rectangular structure. However, the hexagonal meshes provide higher packing density, consistent neighborhood connectivity, and better angular correction compared to the rectilinear square mesh. In this paper, a novel 2-D interpolation hexagonal lattice conversion algorithm has been proposed to develop an efficient hexagonal mesh framework for computer vision applications. The proposed algorithm comprises effective pseudo-hexagonal structures which guarantee to keep align with our human visual system. It provides the hexagonal simulated images to visually verify without using any hexagonal capture or display device. The simulation results manifest that the proposed algorithm achieves a higher Peak Signal-to-Noise Ratio of 98.45 and offers a high-resolution image with a lesser mean square error of 0.59.

Intelligent Demand Forecasting and Replenishment System by Using Nature-Inspired Computing

2017 ◽  
pp. 190-205
Author(s):  
Pragyan Nanda ◽  
Sritam Patnaik ◽  
Srikanta Patnaik
Keyword(s):  
Demand Forecasting ◽  
Apparel Industry ◽  
Computational Time ◽  
Hunting Behaviour ◽  
Sudden Rise ◽  
Nature Inspired Computing ◽  
Multi Agent ◽  
Fashion Apparel ◽  
Group Hunting ◽  
Traditional Approaches

The fashion apparel industry is too diverse, volatile and uncertain due to the fast changing market scenario. Forecasting demands of consumers has become survival necessity for organizations dealing with this field. Many traditional approaches have been proposed for improving the computational time and accuracy of the forecasting system. However, most of the approaches have over-looked the uncertainty existing in the fashion apparel market due to certain unpredictable events such as new trends, new promotions and advertisements, sudden rise and fall in economic conditions and so on. In this chapter, an intelligent multi-agent based demand forecasting and replenishment system has been proposed that adopts features from nature-inspired computing for handling uncertainty of the fashion apparel industry. The proposed system is inspired from the group hunting behaviour of crocodiles such as they form temporary alliances with other crocodiles for their own benefit even after being territorial creatures.

A Survey on Using Nature Inspired Computing for Fatal Disease Diagnosis

2017 ◽  
Vol 8 (2) ◽  
pp. 70-91 ◽  
Author(s):  
Prableen Kaur ◽  
Manik Sharma
Keyword(s):  
Early Diagnosis ◽  
Disease Diagnosis ◽  
Fatal Disease ◽  
Swarm Optimization ◽  
Early Diagnosis Of Cancer ◽  
Nature Inspired Computing ◽  
Human Disorders ◽  
Diagnosis Of Cancer ◽  
Individual Use ◽  
Bee Colonies

Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colonies (ABC) are some vital nature inspired computing (NIC) techniques. These approaches have been used in early prophecy of various diseases. This article analyzes the efficacy of various NIC techniques in diagnosing diverse critical human disorders. It is observed that GA, ACO, PSO and ABC have been successfully used in early diagnosis of different diseases. As compared to ACO, PSO and ABC algorithms, GA has been extensively used in diagnosis of ecology, cardiology and endocrinologist. In addition, from the last six years of research, it has been observed that the accuracy accomplished using GA, ACO, PSO and ABC in the early diagnosis of cancer, diabetes and cardio problems lies between 73.5%-99.7%, 70%-99.2%, 80%-98% and 76.4% to 99.98% respectively. Furthermore, ACO, PSO and ABC are found to be best suited in diagnosing lung, prostate and breast cancer respectively. Moreover, the hybrid use of NIC techniques produces better results as compared to their individual use.

scholarly journals Disruption-Based Multiobjective Equilibrium Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Optimization Algorithms ◽  
Benchmark Problems ◽  
Engineering Optimization ◽  
Test Results ◽  
Nature Inspired Computing ◽  
Disruption Method

Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.

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