A distributed cooperative coevolutionary algorithm for multiobjective optimization

Evolutionary techniques have become one of the most powerful tools for solving multiobjective optimization (MOO) problems. However the computational cost involved in terms of time and hardware often become surprisingly burdensome as the size and complexity of the problem increases. We propose a distributed cooperative coevolutionary algorithm (DCCEA), which evolves multiple solutions in the form of cooperative subpopulations and exploits the inherent parallelism by sharing the computational workload among computers over the network. Through its multiple features such as archiving, dynamic sharing and extending operator, solutions of DCCEA are not only pushed to the true Pareto front but also well distributed. Simulation results show that DCCEA has a very competitive performance and reduces the runtime effectively.