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• W4311538006 abstract "In this paper, we present the Heated Stack Algorithm (HS) which is a population based multi-objective evolutionary algorithm with temperature based on type-2 fuzzy logic meta-heuristic. Temperature plays a vital role in HS being used for two distinct procedures; Sorting and Crossover. In sorting, temperature is combined with the niche distance to determine the rank order of a population front. In crossover, the temperature of two population members are compared to determine the quantity of information to take from each parent. HS is a new optimisation algorithm capable of solving constrained real-world problems. This paper will present the HS application to a real-world capacity planning problem involving networking infrastructure. To proof the algorithm applicability to wider set of problems, we will report the HS results over a subset of the constrained multi objective problems used for optimisation competitions by the IEEE Congress on Evolutionary Computation (CEEC). In these problems we have compared to the popular NSGA-II and its successor NSGA-III. By use of the hyper-volume indicator, we find that the HS outperforms NSGA-II in 84% of cases, and outperforms NSGA-III in 69% of the cases." @default.
• W4311538006 created "2022-12-27" @default.
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• W4311538006 date "2023-01-01" @default.
• W4311538006 modified "2023-09-25" @default.
• W4311538006 title "A heated stack based type-2 fuzzy multi-objective optimisation system for telecommunications capacity planning" @default.
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• W4311538006 doi "https://doi.org/10.1016/j.knosys.2022.110134" @default.
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