In this paper, we have proposed a new optimization algorithm, Memetic improved L-SHADE with a local search pool, MiLSHADE-LSP, a memetic algorithm that combines an improved L-SHADE with a local search pool. Improved L...
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ISBN:
(纸本)9781728121536
In this paper, we have proposed a new optimization algorithm, Memetic improved L-SHADE with a local search pool, MiLSHADE-LSP, a memetic algorithm that combines an improved L-SHADE with a local search pool. Improved L-SHADE modifies several important parameters during the run to encourage exploration in initial stages and to focus later the search around the most promising solutions. The local search pool is responsible to continuously improve the best solutions. MiLSHADE-LSP uses a pool of two different local search, LS, methods, the Broyden-Fletcher-Goldfarb-Shanno method with limited memory, L-BFGS-B, and the Solis-Wets algorithms, with an adaptive mechanism to choose which one of them is applied in each iteration selecting which had obtained a greater improvement last time it was applied. In order to avoid waste LS applications, the proposed algorithm stores a list of individuals that were not previously improved by each LS method. It also includes a restart mechanism to explore new areas when the search is stuck, restarting the population but maintaining the best found solution, and resetting the LS Pool parameters. In the experimental section we have tested and analyzed MiLSHADE-LSP using the proposed benchmark for the competition 100-digit challenge on single objective numerical optimization, obtaining that the LS Pool improves the algorithm, both achieving more optima and with a better performance. Results obtained show that MiLSHADE-LSP is a very competitive algorithm.
Characterizing optimization problems and their properties addresses a key challenge in optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and configuring them. Although several te...
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Characterizing optimization problems and their properties addresses a key challenge in optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and configuring them. Although several techniques have been proposed for extracting features from single-objectiveoptimization problems, the proposed approach offers an alternative look at these problems and their properties. We propose an approach for creating problem representations by utilizing domain-specific filters. These filters have randomly initialized weights and are applied to samples of the optimization problem to extract relevant properties. Proposed features are subsequently used to classify problem instances from the Comparing Continuous Optimizers benchmark demonstrating that problem instances of the same problem tend to be situated near each other in a high-dimensional feature space. Additionally, we demonstrate that the proposed feature extraction method can be used to recognize complex characteristics of optimization functions, including multimodality and the presence of global and funnel structures. We also explore the extent to which these identified features can assist in the selection of algorithms. Our findings reveal that these features are suitable for constructing meta-models for algorithm selection, provided that the problems encountered do not substantially differ from those seen in the training phase. The proposed approach offers a versatile tool for feature extraction, highlighting its applicability across multiple tasks within the domain of optimization.
In numericaloptimization, the characterization of optimization problems and their properties has been a long-standing issue. Overcoming it is a crucial prerequisite for many optimization-related tasks such as buildin...
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In numericaloptimization, the characterization of optimization problems and their properties has been a long-standing issue. Overcoming it is a crucial prerequisite for many optimization-related tasks such as building quality benchmarks, algorithm selection, and algorithm configuration. Several approaches to extracting features from single-objectiveoptimization problems have been proposed but they all have some inherent limitations and thus offer an incomplete look at the problems and their properties. In this work, we extend and improve our previous work on existing topological features that offer a new look at optimization problems where their similarity is quantified in terms of the appearance of topological structures. We show that topologically inspired features are not correlated with existing state-of-the-art landscape feature groups, meaning that they capture different and thus complementary information. Topological features are subsequently used to classify problem instances from the COmparing Continuous Optimizers (COCO) benchmark showing that similar problems most often have similar topological features. Further, we perform a sensitivity analysis of the proposed methodology to its hyperparameters and provide some additional insight into the behavior of the topological features. Lastly, we also investigate topological features and their generalization across different sample sizes.
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