K-ILS additionally employs two new path-based move operators to generate neighboring solutions.
The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators.
We have three types of tabu list and each of them belongs to add, change, and move operators.
The use of short- and long-term memory-based search supported by effective move operators resulted in a powerful combinatorial optimization algorithm.
In particular, the adaptive tabu search phase combines different move operators to collectively conduct neighborhood exploration and an adaptive tabu tenure management mechanism that obviates the task of determining a proper tabu tenure.
Starting from a probabilistically constructed initial solution, BLS uses a Neighborhood Search NSS) procedure based on several specifically designed move operators for local optimization, and employs an adaptive diversification strategy to escape from local optima.
Hyper-heuristics have emerged as such high level methods that explore the space formed by a set of heuristics (move operators) or heuristic components for solving computationally hard problems.
Moving operator site position, for example, could lead to significant changes in the secondary structure of the DNA, TF binding notwithstanding, and therefore lead to additional vertical scaling effects.
In general, the execution semantics might change depending on the way SPEs route elements between operators, since distributed deployments might affect the mutual order of stream elements as they move from operator to operator.
