Ant colony framework for optimal design and scheduling of batch plants

Determination of the critical failure surface for slope stability analysis using ant colony optimization

“…The algorithm is successfully implemented to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force assets. Moreover, frequency assignment problem by Maniezzo and Carbonaro (2000), optimization problems for designing and scheduling of batch plants by Jayaraman et al (2000), quadratic assignment problem by Talbi et al (2001), two-machine flow-shop scheduling problem by T'kindt et al (2003), optimization of the keyboard arrangement by Eggers et al (2003) and solving the mesh-partitioning problem by Korošec et al (2004) are a fewer examples in which the ACO algorithms are used successfully.…”

Ant algorithm for single and multiobjective reliability optimization problems

“…The ant algorithm for continuous function optimization was developed by Bilchev and coworkers [35,36] and has since been applied to various practically relevant problems [37][38][39]. This algorithm consists of a global search carried out by global ants, coupled with a pheromone-mediated local search by local ants.…”

Semantically enabled process synthesis and optimisation

“…Initially, these were deterministic techniques including non-linear programming (NLP) (Achenie and Biegler 1988) and mixed-integer non-linear programming (MINLP) (Kokossis, A. and Floudas 1990;Raman and Grossmann 1991;Kokossis, A. and Floudas 1994), among others. More recent approaches focus on stochastic search based optimisation with perhaps Simulated Annealing (Marcoulaki, E. and Kokossis 1996;Marcoulaki, E. C. and Kokossis 1999;Mehta and Kokossis 2000;Linke and Kokossis 2003a), Tabu Search (Wang, C et al 1999;Linke and Kokossis 2003b;Cavin et al 2004) and Ant Colony (Dorigo et al 1999;Jayaraman et al 2000;Dorigo and Blum 2005) being the most widely applied. The latest advances in stochastic search based on cascading of population and inflection of solutions is particularly attractive as it provides readily access to optimisation solutions at every stage of the process, the search known as the Cascade Algorithm (Labrador-Darder et al 2009;Kokossis, A et al 2011;Cecelja, F et al 2014).…”