Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems

310 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing (2018, methodology): “Compared with other EC algorithms, such as a GA and particle swarm optimization (PSO) [55], [56], the adoption of global shared pheromone in ACS allows the experience information to be spread rapidly among the colony and thus help the cooperation among multiple ants.”
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, methodology): “It is fortunate that the EC paradigm has proved to be one of the most signiﬁcant and promising tools for multiobjective optimization [Zhan et al. 2013; Mukhopadhyay et al. 2014].”
A Survey of Decomposition Methods for Multi-objective Optimization (2014, background): “The coevolutionary algorithms can decompose in objectives, but it requires a different population for every objective [13].”
A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, methodology): “In the past ten years, a lot of multi-objective PSO algorithms have been suggested by addressing the above two issues [24, 25, 28, 32, 43], which can be roughly divided into two categories.”