Purpose: This paper aims to evaluate performance improvements enabled by the S-AMAA (Smart Adaptive Multi-Agent Architecture) framework using two well-known decision-making models: the Technique for Order of Preference by Similarity to the Analytic Hierarchy Process (AHP). This study aims to investigate Multi-Criteria Decision-Making (MCDM) methodologies to examine the extent to which they work in specific circumstances and the general performance of S-AMAA.
Objective: This study aims to evaluate the performance effect of S-AMAA using AHP, two of the most popular decision-making techniques. The study aims to identify the main performance criteria, demonstrate the soundness of the S-AMAA framework, and compare its performance with other methods. The study will also help improve the reliability and applicability of S-AMAA in real-world settings by using rigorous verification and validation methods.
Methodology: In this research, the complex performance factors of S-AMAA will be hierarchically divided using AHP, and the criteria can thus be prioritised using expert knowledge. The ranking of alternatives will then be done using TOPSIS, which will determine the best and most effective configuration of the framework. The methodology integrates two MCDM methods to provide a comprehensive appraisal of S-AMAA's performance, accounting for both subjective and objective factors.
Tryouts: Validation and verification will include testing S-AMAA under various conditions and using performance indicators to assess its flexibility, effectiveness, and scalability. Both real-world applications will be used to collect data and evaluate the consistency of the results across different situations. Such try-outs will enable the study to streamline the decision-making model and ensure the findings are representative and actionable in future applications of S-AMAA across other fields.
International Journal of Engineering in Computer Science
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