A multiagent genetic algorithm for global numerical optimization

By Weicai Zhong, Jing Liu, Mingzhi Xue, L. Jiao. IEEE Trans. Syst. Man Cybern. Part B, 2004

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Novel composition test functions for numerical global optimization (2005, methodology): “According to our experiments, although it generated excellent results on the standard benchmark functions, when we shifted the global optimum to make the optimum to have different values for different dimensions, the performance of the MAGA algorithm significantly deteriorated.”
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, background): “…the global optimization has been very active, producing different kinds of EC for optimization in the continuous domain, such as genetic algorithms [9], evolution strategy [10], evolutionary programming [8], particle swarm optimization [11], immune clonal algorithm [12], differential evolution…”
Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability (2014, methodology): “Based on the multiagent systems, MAGA has been introduced in [14] as a robust approach in dealing with the global numerical optimization problems, where all the agents live in a lattice-like environment.”