Optimization on Combinatorial Configurations Using Genetic Algorithms

Improve Coal Blending Optimization in CFPP by Cromosom and Fitness Function Redefinition of the Genetic Algorithm

“…In a power plant unit, the threshold values for TM, TS, and ash are 34.68, 0.36, and 5.82, respectively. The normalization of each component is expressed in (5). The normalization of the slagging index is done by assigning weights to the slagging index categories (low, medium, high, and severe) as 0, 1, 2, and 3, respectively, and normalizing them to a range of 0 to 1.…”

“…However, GA is computationally expensive and requires careful parameter configuration [6]. GA has demonstrated exemplary performance when implemented in real-world problems, including optimizing CNN architecture with a transfer-learning strategy from parent networks [2], shortest path problem [9],optimizing ANN parameters [10], cryptoanalysis [11], community structure in complex networks [12], multi-objective in packing [13], scheduling [9], combinatorial configuration optimization [5], feature selection Ramdhani 2023 [14], intrution detection suhaimi [15]. There are at least five variants of genetic algorithms, namely real and binary-coded, multiobjective, parallel, chaotic, and hybrid GAs [8].…”

“…The blending problem falls under combinatorial optimization problems, which seek to find the optimal solution by combining various variables with specific constraints. Traditionally, this problem is considered challenging, but evolutionary algorithms, including genetic algorithms, have proven effective in solving it [5].…”

Continuous Extensions On Euclidean Combinatorial Configurations

“…The presented results form the basis for the development of Euclidean combinatorial optimization methods [18,23,25,26,38,39,42,48,49,51,[53][54][55][56][57].Theoretically, it is of interest to develop new approaches to the construction of convex extensions of functions defined on the corresponding C b -sets. At that, it is natural to single out various special classes of C-sets, such as sets of e-configurations of permutation matrices, even, cyclic, or signed permutations, and so on.…”

“…Naturally, both of these directions should be considered integrally. Further, we intend to proceed to the study of genetic algorithms for optimization problems on C-sets in view of previous research [50][51][52]. Of interest are methods of parametric and multicriteria optimization on C-sets, taking into account the results of [5,30].…”