Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems

Świercz, Aleksandra

doi:10.5772/intechopen.69225

Cited by 5 publications

(2 citation statements)

References 53 publications

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“…A hyper-heuristic method does not operate on the solution directly but uses simple procedures, called low-level heuristics, which modify a solution and are evaluated by a function. The algorithm can learn during the search process, whose procedure is better for a particular instance or at a particular time (more on hyper-heuristics can be found in [ 34 – 36 ]). Hyper-heuristics have already been successfully applied to some bioinformatics problems (e.g., [ 37 – 39 ]).…”

Section: Methodsmentioning

confidence: 99%

GRASShopPER—An algorithm for de novo assembly based on GPU alignments

et al. 2018

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Next generation sequencers produce billions of short DNA sequences in a massively parallel manner, which causes a great computational challenge in accurately reconstructing a genome sequence de novo using these short sequences. Here, we propose the GRASShopPER assembler, which follows an approach of overlap-layout-consensus. It uses an efficient GPU implementation for the sequence alignment during the graph construction stage and a greedy hyper-heuristic algorithm at the fork detection stage. A two-part fork detection method allows us to identify repeated fragments of a genome and to reconstruct them without misassemblies. The assemblies of data sets of bacteria Candidatus Microthrix, nematode Caenorhabditis elegans, and human chromosome 14 were evaluated with the golden standard tool QUAST. In comparison with other assemblers, GRASShopPER provided contigs that covered the largest part of the genomes and, at the same time, kept good values of other metrics, e.g., NG50 and misassembly rate.

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Section: Methodsmentioning

confidence: 99%

GRASShopPER—An algorithm for de novo assembly based on GPU alignments

et al. 2018

Self Cite

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“…Nevertheless, it is challenging to decide when to apply a specific method or operator (Osaba et al, 2020). Moreover, there are still difficulties in using the current algorithms due to the existence of multiple parameters and the high sensitivity in their configuration (Swiercz, 2017). Therefore, the choice of a proper search mechanism is crucial since it allows obtaining optimal results with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

Rodríguez-Esparza¹,

Masegosa²,

Oliva³

et al. 2022

Preprint

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Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASA RL ) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The HH implemented improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.

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Designing an adaptive and deep learning based control framework for modular production systems

Panzer,

Gronau

2023

J Intell Manuf

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In today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability.

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Hyper‐Heuristics and Metaheuristics for Selected Bio‐Inspired Combinatorial Optimization Problems

Cited by 5 publications

References 53 publications

GRASShopPER—An algorithm for de novo assembly based on GPU alignments

GRASShopPER—An algorithm for de novo assembly based on GPU alignments

A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

Designing an adaptive and deep learning based control framework for modular production systems

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