70,773 research outputs found
Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning
This paper tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. We compare two evolutionary algorithms (EAs) for optimizing expansion plans: the classic genetic algorithm (GA) with uniform crossover and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) that learns and exploits linkage information between problem variables. We study the impact of incorporating different levels of problem-specific knowledge in the variation operators as well as two constraint-handling techniques: constraint domination and repair mechanisms. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions to the DNEP problem. GOMEA is found to have far more robust performance even when an out-of-box variant is used that doesn't exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for real-world applications like DNEP, EAs that have the ability to model and exploit problem structures, such as GOMEAs and estimation-of-distribution algorithms, should be given priority, especially when problem-specific knowledge is not straightforward to exploit, e.g. in the case of black-box optimization
Denoising Autoencoders for fast Combinatorial Black Box Optimization
Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Autoencoders (AE) are
generative stochastic networks with these desired properties. We integrate a
special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate
the performance of DAE-EDA on several combinatorial optimization problems with
a single objective. We asses the number of fitness evaluations as well as the
required CPU times. We compare the results to the performance to the Bayesian
Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a
generative neural network which has proven competitive with BOA. For the
considered problem instances, DAE-EDA is considerably faster than BOA and
RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is
higher than for BOA, but competitive with RBM-EDA. These results show that DAEs
can be useful tools for problems with low but non-negligible fitness evaluation
costs.Comment: corrected typos and small inconsistencie
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