Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems

By Renato Antonio Krohling, Leandro dos S. Coelho. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2006

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Dung beetle optimizer: a new meta-heuristic algorithm for global optimization (2022, methodology): “There are several optimization algorithms previously applied to the welded beam design problem such as the HHO algorithm [17], GSA [46], WOA [16], MVO algorithm [40], MPA [48], CPSO algorithm [49], HS algorithm [50], and SSA [22].”
Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2015, methodology): “Some of the works that solved this problem are as follows: GA [31–33], CPSO [34], HS [35], Richardson’s random method, Simplex method, Davidon–Fletcher–Powell, and Grifﬁth and Stewart’s successive linear approximation [36].”
Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems (2010, background): “…probability distributions to generate random numbers to updating the velocity equation of PSO have been proposed (Coelho & Alotto, 2008; Coelho & Herrera, 2007; Coelho & Lee, 2008; Higashi & Iba, 2003; Kennedy, 2003; Krohling, 2004; Krohling & Coelho, 2006a, 2006b; Secrest & Lamont, 2003).”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, background): “enhance the algorithm performance is still a crucial problem [48].”