1. What are the contributions in "A hierarchical particle swarm optimizer and its adaptive variant" ?
A hierarchical version of the particle swarm optimization ( PSO ) metaheuristic is introduced in this paper.. In the new method called H-PSO, the particles are arranged in a dynamic hierarchy that is used to define a neighborhood structure.. Depending on the quality of their so-far best-found solution, the particles move up or down the hierarchy.. This gives good particles that move up in the hierarchy a larger influence on the swarm.. The authors introduce a variant of H-PSO, in which the shape of the hierarchy is dynamically adapted during the execution of the algorithm.. Another variant is to assign different behavior to the individual particles with respect to their level in the hierarchy.
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2. What future works have the authors mentioned in the paper "A hierarchical particle swarm optimizer and its adaptive variant" ?
One interesting topic for future research is to identify conditions that could trigger the decrease of the branching degree for AH-PSO so that it could autonomously adapt to the state of the optimization process.
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3. How many swaps do you find between levels 1 and 2?
In the test runs the ratio of swaps that occur between levels 0 and 1 to swaps that occur between levels 1 and 2 is approximately 0.8 to 1, depending on the objective function.
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4. What is the main fraction of all swaps done during a run?
Except for the Rastrigin function, the main fraction of all swaps is done very early during a run [99% of all swaps are been done until iteration 1680 (Griewank), 2470 (Schaffer), and 2240 (Ackley)].
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