Application of Genetic Algorithms and Ant Colony Optimization for Modelling of E. coli Cultivation Process

Metaheuristic Techniques for Optimization of anE. ColiCultivation Model

“…the Aco algorithm achieves a much better solution at the beginning, because it is a constructive method. With time, the achieved values of the objective function by both algorithms become close to each other, supporting our previous work (37), where the execution time was 50 minutes and a similar trend was observed.…”

“…the same problem is solved in another work of ours (37) by using different objective function -hausdorff distance between modeled and measured data. in this case the Aco algorithm performs slightly better than GA (37).…”

“…1. Pseudocode for Aco (37). the transition probability p i,j , to choose the node j when the current node is i, is based on the heuristic information η i,j and the pheromone trail level τ i,j of the move:…”

“…2. Pseudocode for GA (37). the population at time t is represented by the timedependent variable P, with the initial population of random estimates being P(0).…”

“…Following Glover (19), "metaheuristics in their modern forms are based on a variety of interpretations of what constitutes intelligent search". the family of metaheuristics includes, but is not limited to, adaptive memory procedures, tabu search, ant systems, greedy randomized adaptive search, variable neighborhood search, evolutionary methods, genetic algorithms, scatter search, neural networks, simulated annealing, and their hybrids (37). in this paper Genetic Algorithms (GA) (20,21) and Ant colony optimization (Aco) (13,14) are chosen as two of the most popular metaheuristics.…”