On the Performance of Exhaustive Search with Cooperating agents

TL;DR

This paper analyzes how cooperation among multiple search agents affects the performance of exhaustive search, highlighting that increased cooperation improves efficiency and that heterogeneous agents can perform equally well with proper subregion allocation.

Contribution

It provides an analysis of cooperative strategies in exhaustive search with multiple agents, emphasizing the impact of cooperation level and agent heterogeneity on performance.

Findings

Performance improves with increased cooperation

Heterogeneous agents can match homogeneous performance with proper subregion division

Cooperation strategies significantly influence search efficiency

Abstract

Despite the occurrence of elegant algorithms for solving complex problem, exhaustive search has retained its significance since many real-life problems exhibit no regular structure and exhaustive search is the only possible solution. The advent of high-performance computing either via multicore processors or distributed processors emphasizes the possibility for exhaustive search by multiple search agents. Here we analyse the performance of exhaustive search when it is conducted by multiple search agents. Several strategies for cooperation between the search agents are evaluated. We discover that the performance of the search improves with the increase in the level of cooperation. Same search performance can be achieved with homogeneous and heterogeneous search agents provided that the length of subregions allocated to individual search regions follow the differences in the speeds of heterogeneous search agents.