Optimized Technique for College Students Job Searching Strategies Using Fuzzy Logic Control with Cuckoo Search Algorithm

Abstract

College students face uncertainties during job searches due to a lack of career planning, unclear objectives, and ineffective search strategies, leading to poor employment outcomes. Fuzzy Control (FC) based Job Search Strategies (JS2) are proposed in this research as an optimized technique named FC-JS2-TSC. This technique combines Takagi-Sugeno (TS) fuzzy inference with Cuckoo (C) search optimization. The primary goals are improving individualized advice and creating an integrated system to deal with job search concerns. The FC uses fuzzy logic and sets to model uncertainties such as vague job desires and ever-changing market circumstances. Individual student profiles and preferences are used to fine-tune methods by cuckoo search. Through experimental validation, we can see that FC-JS2-TSC outperforms previous methods in terms of both job strategy selection and results. As a measure of system efficacy, the results demonstrate a high Cronbach's alpha reliability of 0.96, a low RMSEA of 0.04 and 96.6% regarding job offers. By adjusting tactics in response to uncertainty, the innovative FC-JS2-TSC algorithm facilitates data-driven, personalized decision-making, ultimately leading to more efficient job searches. It has an integrated design that combines optimization with fuzzy logic's uncertainty handling to ensure students have the best possible chance of success in their job searches.