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Related Experiment Video

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Ternary compound ontology matching for cognitive green computing.

Wei-Min Zheng¹, Qing-Wei Chai¹, Jie Zhang²

¹College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Mathematical Biosciences and Engineering : MBE

|July 2, 2021

Summary

Cognitive green computing (CGC) introduces a compact evolutionary algorithm (CEA) to reduce memory usage in resource-limited hardware. This CEA effectively addresses complex problems like ternary compound ontology matching, enhancing efficiency.

Keywords:

cognitive green computing compact evolutionary algorithm ternary compound ontology matching

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Area of Science:

Computer Science
Environmental Science
Artificial Intelligence

Background:

Cognitive green computing (CGC) aims to minimize environmental impact from computing systems.
Evolutionary algorithms (EA) are powerful optimization tools but suffer from high memory demands.
Memory limitations hinder EA application in resource-constrained environments.

Purpose of the Study:

To develop a memory-efficient evolutionary algorithm for Cognitive Green Computing.
To adapt the algorithm for solving the ternary compound ontology matching problem.

Main Methods:

Propose a compact evolutionary algorithm (CEA) with reduced memory footprint.
Implement CEA using compact encoding and an efficient evolving mechanism.
Test CEA performance on six datasets comprising nine ontologies for ontology matching.

Main Results:

CEA significantly reduces memory consumption compared to traditional population-based EAs.
Experimental results demonstrate the effectiveness of CEA in solving the ontology matching task.
The proposed method shows promise for memory-limited hardware applications in CGC.

Conclusions:

The developed CEA offers a viable solution for memory-intensive optimization problems within CGC.
CEA's efficiency makes it suitable for applications requiring substantial computational resources on limited hardware.
This research advances the integration of efficient algorithms into green computing practices.