It is important to harmonize effectively the behaviors of the agents in the multi-agent system (MAS) to complete the solution process. The co-evolution computing techniques, inspired by natural selection and genetics,...It is important to harmonize effectively the behaviors of the agents in the multi-agent system (MAS) to complete the solution process. The co-evolution computing techniques, inspired by natural selection and genetics, are usually used to solve these problems. Based on learning and evolution mechanisms of the biological systems, an adaptive co-evolution model was proposed in this paper. Inner-population, inter-population, and community learning operators were presented. The adaptive co-evolution algorithm (ACEA) was designed in detail. Some simulation experiments were done to evaluate the performance of the ACEA. The results show that the ACEA is more effective and feasible than the genetic algorithm to solve the optimization problems.展开更多
基金Project of Shanghai Committee of Science and Technology, China ( No.08JC1400100, No. QB081404100)Leading Academic Discipline Project of Shanghai Municipal Education Commission, China (No.J51901)
文摘It is important to harmonize effectively the behaviors of the agents in the multi-agent system (MAS) to complete the solution process. The co-evolution computing techniques, inspired by natural selection and genetics, are usually used to solve these problems. Based on learning and evolution mechanisms of the biological systems, an adaptive co-evolution model was proposed in this paper. Inner-population, inter-population, and community learning operators were presented. The adaptive co-evolution algorithm (ACEA) was designed in detail. Some simulation experiments were done to evaluate the performance of the ACEA. The results show that the ACEA is more effective and feasible than the genetic algorithm to solve the optimization problems.
