An Evolutionary Approach for Optimizing Hierarchical Multi-Agent System Organization

TL;DR

This paper introduces a novel hierarchical genetic algorithm to optimize multi-agent system organization, effectively finding high-utility structures in large search spaces more efficiently than traditional methods.

Contribution

It presents a new hierarchical genetic algorithm with specialized operators for designing optimal multi-agent system organizations, improving efficiency and solution quality.

Findings

The algorithm finds competitive, high-utility organizational structures.

It outperforms traditional genetic operators in consistency and quality.

The method is more computationally efficient in large search spaces.

Abstract

It has been widely recognized that the performance of a multi-agent system is highly affected by its organization. A large scale system may have billions of possible ways of organization, which makes it impractical to find an optimal choice of organization using exhaustive search methods. In this paper, we propose a genetic algorithm aided optimization scheme for designing hierarchical structures of multi-agent systems. We introduce a novel algorithm, called the hierarchical genetic algorithm, in which hierarchical crossover with a repair strategy and mutation of small perturbation are used. The phenotypic hierarchical structure space is translated to the genome-like array representation space, which makes the algorithm genetic-operator-literate. A case study with 10 scenarios of a hierarchical information retrieval model is provided. Our experiments have shown that competitive baseline structures which lead to the optimal organization in terms of utility can be found by the proposed algorithm during the evolutionary search. Compared with the traditional genetic operators, the newly introduced operators produced better organizations of higher utility more consistently in a variety of test cases. The proposed algorithm extends of the search processes of the state-of-the-art multi-agent organization design methodologies, and is more computationally efficient in a large search space.