Automated concurrent negotiations: An artificial bee colony approach

In Electronic Marketplaces (EMs), a number of unknown entities can interact to conclude purchase actions. Interactions are, usually, between buyers and sellers. Both groups of entities (e.g., buyers, sellers) aim to acquire items in the most profitable price. The discussed interactions are realized in the form of negotiations over a number of items characteristics. In this paper, we focus on the buyer side and deal with automated multi-issue concurrent negotiations. Such negotiations are between buyers and multiple sellers having in their property specific items. Each buyer negotiates with a number of sellers trying to achieve the most profitable value for a number of items' characteristics. We propose an optimization model for achieving the maximum possible utility. Our method adopts the principles of the Artificial Bee Colony (ABC) algorithm that offers a number of advantages compared to other Swarm Intelligence (SI) methods (e.g., Particle Swarm Optimization - PSO). The buyer, based on a number of threads, tries to find the optimal agreement when negotiating with a group of sellers. Every agreement, realized with a specific seller, results a utility for the buyer concluded over a weighted scheme on the items characteristics. Each thread adopts a weights adaptation model for optimizing the utility. A set of experiments reveal the strengths and weaknesses of the proposed model. We also report on a comparison assessment between the proposed method and other efforts found in the respective literature. (C) 2016 Elsevier B.V. All rights reserved.