Proceedings of IEEE International Conference on Systems, Man and Cybernatics, 2013
The following topics were dealt with: human/computer interfaces; texture, depth and motor perception; neural nets; fuzzy systems; learning; product/process design; simulation; robotics; visual system cybernetics; batch processes; image compression and interpretation; AI applications; fuzzy adaptive control; decision modelling; agile manufacturing; service sector; inductive algorithms; complex systems; Petri nets; real time imaging; KBS; machine recognition; requirements engineering; inspection and shop floor control; environmental decision making; medicine; supervisory control; discrete event systems; power systems; software methods; heuristic search; vision systems; database systems; information modelling; facility design and material handling; conflict resolution; emergency management; genetic algorithms; decision making and path planning; IVHS; senses approximation; intelligent user interface; robust controllers for mechanical systems; cognitive and learning systems; command and control systems; pilot associate systems; neural net applications; real time systems; mobile robot visual processes; medical applications; utility energy systems; machine recognition; computing systems design; software engineering; military applications; data analysis; stochastic processes; guided vehicles; and stability and compensation.
Abstract— In this paper a new multi-agent genetic algorithm for multi-objective optimization (MAGAMO) is presented. The algorithm based on the dynamical interaction of synchronized agents which are interdepended genetic algorithms (GAs) having own separate evolutions of their populations. This approach has some similarities with well known “island model” of GA. In both methods is used a migration of individuals from agents (“islands”) to the main process (“continent”). In contrast, the intelligent agents in MAGAMO are able to decompose the dimensions space to form evolutions of subpopulations (instead of distribution of initial population as in the standard “island model”). In the same time, the main (central) process is responsible for the coordination of agents only and their selection according Pareto rules (without evolution). Intelligent agents seek local suboptimal solutions for a global optimization, which will be completed in the result of the interaction of all agents. In the result of this, the amount of needed recalculating the fitness-functions can be significantly reduced. It is especially important for the multi-objective optimization related to a large-scale problem. Besides, the proposed approximating approach allows solving complex optimization problems for real big systems (like an oil company, plants, corporations, etc.).