An Artificial Bee Colony Based Algorithm for Continuous Distributed Constraint Optimization Problems

TL;DR

This paper introduces a novel Artificial Bee Colony-based algorithm for solving continuous distributed constraint optimization problems, demonstrating improved performance over existing methods through theoretical proof and empirical evaluation.

Contribution

The paper presents a new population-based solver for C-DCOPs inspired by ABC, including a novel exploration method and proof of its anytime property.

Findings

The proposed algorithm outperforms state-of-the-art C-DCOPs algorithms in experiments.

The approach is proven to be an anytime algorithm.

Significant improvements in solution quality are demonstrated empirically.

Abstract

Distributed Constraint Optimization Problems (DCOPs) are a frequently used framework in which a set of independent agents choose values from their respective discrete domains to maximize their utility. Although this formulation is typically appropriate, there are a number of real-world applications in which the decision variables are continuous-valued and the constraints are represented in functional form. To address this, Continuous Distributed Constraint Optimization Problems (C-DCOPs), an extension of the DCOPs paradigm, have recently grown the interest of the multi-agent systems field. To date, among different approaches, population-based algorithms are shown to be most effective for solving C-DCOPs. Considering the potential of population-based approaches, we propose a new C-DCOPs solver inspired by a well-known population-based algorithm Artificial Bee Colony (ABC). Additionally, we provide a new exploration method that aids in the further improvement of the algorithm's solution quality. Finally, We theoretically prove that our approach is an anytime algorithm and empirically show it produces significantly better results than the state-of-the-art C-DCOPs algorithms.