The traveling salesman problem with distances one and two
Mathematics of Operations Research
Future Generation Computer Systems
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Special Genetic Identification Algorithm with smoothing in the frequency domain
Advances in Engineering Software
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This paper presents the application of ant colony optimization (ACO) for the multi-objective optimization of hybrid laminates for obtaining minimum weight and cost. The investigated laminate is made of glass-epoxy and graphite-epoxy plies to combine the lightness and economical attributes of the first with the high-stiffness property of the second using a modified variation of ACO so called the elitist ant system (EAS) in order to make the tradeoff between the cost and weight as the objective functions. First natural frequency was considered as a constraint. The obtained results using the EAS method including the Pareto set, optimum stacking sequences, and the number of plies made of either glass or graphite fibers were compared with those using the genetic algorithm (GA) and any colony system (ACS) reported in literature. The comparisons confirm the advantage of hybridization and showed that the EAS algorithm outperformed the GA and ACS in terms of function's value and constraint accuracy.