Multiobjective genetic algorithm strategies for electricity production from generation IV nuclear technology

Temperature impact assessment on struvite solubility product: A thermodynamic modeling approach

“…(2), (8)- (16) and (19)-(23)) using the already mentioned genetic algorithm [32] for each experimental point. The problem is formulated as an optimization problem with Eq.…”

“…The genetic algorithm used in the MULTIGEN library [32], which is a variant of the well-known NSGA II [35]. This algorithm makes it possible to reduce the value of the criterion to approximately 10 −3 for a population of 100 individuals along 100 generations.…”

“…The initialization step still constitutes a difficulty: to circumvent it, a multicriterion genetic algorithm embedded in the so-called MULTIGEN library [32] was used: each squared equation of the model is considered as an objective, and each unknown is viewed as an optimization variable.…”

Multi-Objective Genetic Algorithms for Chemical Engineering Applications

“…One of the most efficient genetic algorithms is NSGA II [14], an upgrade of NSGA, which estimates the density of solutions surrounding a particular one. According to [11], the performance of this algorithm is efficient so that NSGA II has gained a lot of popularity in the last few years The used procedures are part and parcel of an intern algorithm library (MULTIGEN) developed in [21]. The MULTIGEN tools, written in VBA, use Excel sheets as an interface.…”

“…The MULTIGEN library involves several algorithms that can solve different types of problems involving structure problems and different variables (continuous, integer, binary): six different algorithms are currently implemented. More details can be found in [21]. Only the key points are briefly recalled in what follows.…”