A distributed co-evolutionary particle swarm optimization algorithm
D.S. Liu, Kay Chen Tan, Weng Khuen Ho · 2007
This paper introduces a distributed co- evolutionary particle swarm optimization algorithm (DCPSO). In DCPSO, the population is decomposed into the subpopulations that are each responsible for optimizing one parameter, and co-evolve in a competitive manner. Such co-evolution mechanism with both cooperation and competition are designed to be effective and efficient in solving multi-objective (MO) problems under distributed computation. The competition mechanism also indirectly helps DCPSO to overcome the fault-tolerance constraint in distributed computation. DCPSO shows substantial speedup from non-distributed version by sharing workload among computational nodes. In addition, a dynamic load balancing mechanism is used to further speed up the total runtime by minimizing the amount of idle time in each node.