A real-world-like evolutionary algorithm on the cloud-computing environment

This paper develops a multiobjective evolutionary algorithm on the cloud computing environment to help planners solve multiobjective problems more efficiently and effectively. The cloud environment is emulated as a virtualized biological world with several isolated regions. The main population initially continues evolutionary processes as the most widely-known evolutionary algorithms. To yield both exploration and exploitation, two processes, such as migration and interaction, are deployed. In the process of migration, local optimal solutions can migrate to form new populations so that the search space can be expanded. To overcome the disadvantages in isolated evolutionary algorithms, the individuals in different populations will interact stochastically in the interaction process. Taking the advantage of cloud computing environment, planners can take less effort on deploying both computation power and storage space. Instead, the planners can focus on the design of encoding, crossover, and mutation. Also, it can further applied in various complicated applications more practically.