Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems

TL;DR

This paper introduces a hybrid autonomous multi-agent system that integrates efficient metaheuristics to improve performance on complex continuous optimization problems, extending the capabilities of traditional EMAS.

Contribution

It proposes a novel hybrid version of EMAS that incorporates multiple metaheuristics and defines rules for hybrid step execution, enhancing optimization effectiveness.

Findings

Improved performance on difficult continuous-optimization benchmarks.

Effective integration of multiple metaheuristics into EMAS.

Demonstrated advantages over standard EMAS in complex problems.

Abstract

Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relevant Markov-Chain based model. Now the research continues on introducing autonomous hybridization into EMAS. This paper focuses on a proposed hybrid version of the EMAS, and covers selection and introduction of a number of hybrid operators and defining rules for starting the hybrid steps of the main algorithm. Those hybrid steps leverage existing, well-known and proven to be efficient metaheuristics, and integrate their results into the main algorithm. The discussed modifications are evaluated based on a number of difficult continuous-optimization benchmarks.