Flock-based Evolutionary Multi-Agent System in solving noisy multi-objective optimization problems

It has been already proofed and discussed in our previous works that thanks to its computational as well as implemental simplicity evolutionary multi-agent system (EMAS) approach can be easily adjusted and widely used for solving almost any type of multi-objective optimization task. Unfortunately, results obtained by distributed and decentralized agent-based evolutionary heuristic approach applied for solving multi-objective optimization problems (MOOPs) can be limited by such factors, as stagnation of agents\´ evolution or problems with distributing agents evenly over the whole approximation of the Pareto frontier. In the course of this paper three main shortcomings of EMAS-based approach are diagnosed and ideas of possible solutions of identified problems with the use of "flock-based" mechanisms are presented. Next, one of many possible realization of flock-based ideas i.e. so-called densityplus realization of floEMAS paradigm is discussed. Finally, experimental results obtained by floEMAS system in solving noisy multi-objective optimization tasks are presented. The goal of this paper is: to discuss shortcomings identified in applying EMAS for solving MOOPs, to present flock-based mechanisms allowing for overcoming such shortcomings, to discuss one of possible realization of floEMAS approach and to present the behavior of proposed floEMAS realization in noisy environment - since in the case of EMAS-based approaches there is no need to introduce any additional mechanisms to solve efficiently noisy problems.