Bi-Criterion Optimization with Multi Colony Ant Algorithms

By Steffen Iredi, Daniel Merkle, Martin Middendorf. International Conference on Evolutionary Multi-Criterion Optimization, 2001

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A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “forms MOEA/D-GA [9] with conventional genetic operators and a local search operator [19] on several test instances of MOKP, and bi-criterion ant algorithm [88] on several test”
Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection (2004, methodology): “Further, Iredi et al. (2001) and McMullen (2001) developed an Ant Colony system for optimization problems that consist of two objectives only and applied it to sequencing problems.”
A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP (2007, methodology): “did in [32] for a problem with two criteria.”
Ant colony optimization for resource-constrained project scheduling (2000, methodology): “The ACO approach has been applied recently to scheduling problems, as job-shop, flow-shop, and single machine tardiness problems (see [1], [5], [7], [18], [28], [29], [35], [39]).”
Ant Colony Optimization: Overview and Recent Advances (2018, background): “The ﬁrst general ACO approach targeted to such problems is due to Iredi et al. [93], who discussed various alternatives to apply ACO to multiobjective problems and presented results with a few variants for a bi-objective scheduling problem.”