Optimization and defect identification using distributed evolutionary algorithms

On the Computational Cost and Complexity of Stochastic Inverse Solvers

“…. ., is followed arbitrarily closely by the trajectory of the SGA population, in the sense of inequality (11). Moreover, we may conclude that the trajectory of heuristic iterations represents the maximum search ability performed by the infinite µ → +∞ population among all SGA searches represented by the same heuristic H. Further, if the heuristic H is focusing (see Definition 3), then we may infer that its fixed points represent the limit search possibility of the aforementioned class of search algorithms.…”

“…One possibility is to use stochastic metaheuristics, which are solution methods that orchestrate an interaction between local improvement procedures and higher-level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space [25,37,39]. Among metaheuristic search strategies, we highlight evolutionary algorithms (EAs; we point the reader to the works of Burczyński and Beluch [10], Burczyński et al [11], and Meruane and Heylen [36] for some examples of using EAs to solve inverse problems). Our particular interest is devoted to EAs that can handle misfit multimodality, such as niching and sequential niching strategies (see, e.g., the work of Mahfoud [35]), Hierarchic Genetic Strategy HGS [5,7,24,29,30,62], and adaptive, stochastic multi-start method (see, e.g., the work of Telega et al [76]).…”

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

“…In [12,13,75], the population is divided into several subpopulations, which run on different master processors and communicate in some specific time. For each subpopulation, the master sends the individual evaluation tasks to its own slave processors so as to further improve parallelization grain.…”

Crack identification in plates using 1-D discrete wavelet transform

“…acoustic emission (Rogers, 2005), X-rays (Shinoba et al, 2004), eddy current (Gros, 1995), ultrasonography (Zhang et al, 2004), magnetic field (Lee et al, 2004) or soft computing methods such as artificial neural networks (Waszczyszyn and Ziemiań-ski, 2001) and evolutionary algorithms (Burczyński et al, 2004). The traditional approach, based on the analysis of natural frequencies (Dems and Mróz, 2001) and modal shapes of structure vibrations (Ostachowicz and Kaczmarczyk, 2001), is still used and further developed.…”