MHACO: a Multi-Objective Hypervolume-Based Ant Colony Optimizer for Space Trajectory Optimization

Acciarini, Giacomo; Izzo, Dario; Mooij, Erwin

doi:10.1109/cec48606.2020.9185694

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…The exploration of the design variable space and the generation of the Pareto fronts for both the optimisation run are performed through the exploitation of a Multi-Objective Hypervolume-Based Ant Colony Optimisation (MHACO) algorithm [1]. The ESA pagmo [2] optimisation package has been exploited for that purpose.…”

Section: Optimisation Analysis and Resultsmentioning

confidence: 99%

Cislunar distributed architectures for communication and navigation services of lunar assets

et al. 2022

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Section: Optimisation Analysis and Resultsmentioning

confidence: 99%

Cislunar distributed architectures for communication and navigation services of lunar assets

et al. 2022

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“…The Multi-objective Hypervolume-based Ant Colony Optimizer (MACO) is a multi-objective optimization algorithm that extends the GACO algorithm described above, combining hypervolume computation and non-dominated fronts for ranking individuals [73].…”

Section: Multi-objective Global Optimization Algorithmsmentioning

confidence: 99%

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

Mohasci,

Torok,

Saray

et al. 2024

Preprint

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Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.

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“…In [12], the research presents an ant colony optimization-based routing protocol for multi-agents that efficiently copes network enabled resources in real-time .The suggested approach is utilised to control pheromone updates and evaporation rates in addition to ant movement management and determining their future location. Energy left, buffer size, traffic rate, and distance are some of the important factors that are considered while choosing the next location under various circumstances.…”

Section: Related Workmentioning

confidence: 99%

FTDA: Performance Enhancement of WSN using Fuzzy based Traffic Data Analysis

Anuradha P. Gharge

2023

IJRITCC

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In the present time in Wireless Sensor Network plays an essential role in the monitoring of different physical phenomena. Monitoring of city traffic data analysis is very important in different metro cities due to rapid increase in population. This research work proposes a model for traffic data analysis using wireless sensor network incorporated with fuzzy technique. The proposed model is tested for performance parameters such as node dead rate , data packed received. The proposed model improved the efficiency compared to existing techniques of the WSN network for traffic data collection and analysis.

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MHACO: a Multi-Objective Hypervolume-Based Ant Colony Optimizer for Space Trajectory Optimization

Cited by 12 publications

References 15 publications

Cislunar distributed architectures for communication and navigation services of lunar assets

Cislunar distributed architectures for communication and navigation services of lunar assets

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

FTDA: Performance Enhancement of WSN using Fuzzy based Traffic Data Analysis

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