An empirical study about the usefulness of evolution strategies to solve constrained optimization problems

By Efrén Mezura-Montes, Carlos Artemio Coello Coello. International Journal of General Systems, 2008

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A novel Random Walk Grey Wolf Optimizer (2019, methodology): “GSA [29], PSO (He andWang) [50], GA (Coello) [46], GA (Deb) [51], ES [52], Lagrangian Multiplier [40] and Branch and Bound (Sandgen) [53] algorithms are employed on pressure vessel problem.”
Stochastic Fractal Search: A powerful metaheuristic algorithm (2015, methodology): “Deb [ 48 ] , Coello [ 49 ] and Coello and Montes [ 40 ] solved this problem using GA - based methods .”
Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “8 Schematic representation of spring pared with Grey Wolf Optimization (GWO) (Mirjalili et al. 2014), Gravitational Search Algorithm (GSA) (Rashedi et al. 2009), PSO (He and Wang 2007), ES (Mezura-Montes and Coello 2008), GA (Coello 2000a), HS (Mahdavi et al. 2007), and DE (Huang et al. 2007).”
Multi-Verse Optimizer: a nature-inspired algorithm for global optimization (2015, result): “We compare the results to PSO [48], GA [49–51], ES [52], DE [53], and ACO [54].”
Grey Wolf Optimizer (2014, methodology): “The heuristic methods that have been adopted to optimize this problem are: PSO [58], GA [57, 60, 69], ES [59], DE [62], and ACO [70].”
A novel heuristic optimization method: charged system search (2010, methodology): “Recently, Montes and Coello [ 24 ] and the authors [1] used evolution strategies and an improved ant colony optimization to solve this problem, respectively.”