Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness
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Cited by (73)
Design of Fresnel acoustic reflector for sub-wavelength broadband sound diffusion
2024, Materials and DesignEvolutionary ORB-based model with protective closing strategies
2021, Knowledge-Based SystemsCitation Excerpt :However, an uneven solution space with multiple peaks cannot guarantee a gradient indicating the correct direction. GA is a heuristic approach in evolutionary computation [22], which has proven highly effective in nonconvex multi-peak optimization problems [23]. This approach is based on the concept of survival by natural selection [24], in which a strong individual (a strong parameter set) has a higher likelihood of survival.
A meta optimisation analysis of particle swarm optimisation velocity update equations for watershed management learning
2018, Applied Soft ComputingCitation Excerpt :The field of meta optimisation dates back to 1978 when it was first applied to tune a genetic algorithm [32]. In the years since, meta optimisation has been applied to ant colony optimisation [6], differential evolution [34], COMPLEX-RF [22], particle swarm optimisation [31,35] and genetic algorithms [14,4,20]. There are limitations to the previous studies conducted in applying meta optimisation to PSO.
Meta-harmony search algorithm for the vehicle routing problem with time windows
2015, Information SciencesCitation Excerpt :The main role of the meta-HSA optimizer is to adjust HSA parameter values, the local search algorithm type and the local search configurations (parameter values and the neighborhood structures) during the search without any external influence. The main difference between the proposed meta-HSA and the existing ones [24–28] is that the proposed meta-HSA is used to adjust the parameter values, local search types and local search configurations (parameter values and neighborhood operators) while the existing meta-optimizers only adjust the parameter values. Furthermore, the proposed meta-HSA adjusts these components and configurations in an online manner, while existing ones use training and testing instances, which might make them well suited to the training instances only.
Field performance of a genetic algorithm in the settlement prediction of a thick soft clay deposit in the southern part of the Korean peninsula
2015, Engineering GeologyCitation Excerpt :A back-analysis method based on a genetic algorithm (GA) can be used as a parallel and global search tool that emulates natural genetic operators. GAs generally show better performance when searching for a solution than conventional optimization algorithms because GAs, which make use of an entire set of solutions spread throughout the solution space, are less affected by local optima (Holland, 1975; Goldberg, 1989; Keane, 1995). Park et al. (2009) showed that the GA back-analysis method has the advantage of robustly searching for a global solution while avoiding local solutions compared with conventional optimization schemes in a multi-dimensional consolidation problem with three consolidation layers.
Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms
2014, Journal of Natural Gas Science and Engineering