Agent Based Processing of Global Evaluation Function

TL;DR

This paper introduces a parallel processing approach using agents and a global evaluation function to improve load balancing and constraint satisfaction in networked environments, demonstrating the benefits of agent-based parallelism.

Contribution

It proposes a novel agent-based parallel processing method utilizing a global evaluation function for constraint satisfaction problems, highlighting optimal agent distribution for maximum efficiency.

Findings

Parallel agent-based processing enhances load balancing.

Optimal number of agents improves constraint satisfaction.

System outperforms traditional solutions in efficiency.

Abstract

Load balancing across a networked environment is a monotonous job. Moreover, if the job to be distributed is a constraint satisfying one, the distribution of load demands core intelligence. This paper proposes parallel processing through Global Evaluation Function by means of randomly initialized agents for solving Constraint Satisfaction Problems. A potential issue about the number of agents in a machine under the invocation of distribution is discussed here for securing the maximum benefit from Global Evaluation and parallel processing. The proposed system is compared with typical solution that shows an exclusive outcome supporting the nobility of parallel implementation of Global Evaluation Function with certain number of agents in each invoked machine.