SOLVING THE TASK OF LOCAL OPTIMA TRAPS IN DATA MINING APPLICATIONS THROUGH INTELLIGENT MULT-AGENT SWARM AND ORTHOPAIR FUZZY SETS
Abstract
Local optima traps pose a significant challenge in optimizing complex problems, particularly in data mining applications, where traditional algorithms may get stuck in suboptimal solutions. This study addresses this issue by combining the power of intelligent multi-agent swarm algorithms and orthopair fuzzy sets to enhance optimization processes. We propose a novel approach that leverages the collective intelligence of a multi-agent swarm system, enabling effective exploration and exploitation of solution spaces. Additionally, orthopair fuzzy sets are introduced to model and represent uncertainties inherent in data mining tasks, providing a more robust optimization framework. Our work contributes to the advancement of optimization techniques in data mining by offering a synergistic solution to local optima traps. The integration of intelligent multi-agent swarms and orthopair fuzzy sets enhances the algorithm’s adaptability and resilience, leading to improved convergence and better solutions. Experimental results demonstrate the efficacy of our proposed approach in overcoming local optima traps, showcasing superior performance compared to traditional algorithms. The hybrid system exhibits increased convergence rates and consistently discovers more accurate and diverse solutions across various data mining scenarios.

Authors
Reddi Kiran Kumar1, P. Chengamma2, A. Senthil Kumar3, G. Sai Chaitanya Kumar4
Krishna University, India1, Annamacharya Institute of Technology and Sciences, India2, Shri Vishnu Engineering College for Women, India3, DVR & Dr. HS MIC College of Technology, India4

Keywords
Local Optima Traps, Data Mining, Intelligent Multi-Agent Swarm, Orthopair Fuzzy Sets, Optimization
Yearly Full Views
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember
0134100000000
Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 14 , Issue: 3 , Pages: 3263 - 3268 )
Date of Publication :
January 2024
Page Views :
76
Full Text Views :
18

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.