Numerical Experiments on Pareto-optimal Task Assignment Representations by Tabu-based Evolutionary Algorithm

Meta-heuristics like evolutionary algorithms require extensive numerical experiments to adjust their capabilities of solving decision making problems. Evolutionary algorithm can be applied for finding solution in distributed computer systems. Reliability and the load balancing are crucial factors for a quality evaluation of distributed systems. Load balancing of the Web servers can be implemented by reduction of the workload of the bottleneck computer what improves both a performance of the system and the safety of the bottleneck computers. An evolutionary algorithm based on a tabu search procedure is discussed for multi-criteria optimization of distributed systems A tabu mutation is applied for minimization the workload of the bottleneck computer. It can be obtained by task assignment as well as selection of suitable computer sorts. Moreover, a negative selection procedure is developed for improving non-admissible solutions. Extended numerical results are submitted. Key-Words: Evolutionary algorithm, Multi-criterion optimization, Distributed systems, Artificial intelligence, Pareto solutions