Memetic algorithms for mapping <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2012" altimg="si269.svg"><mml:mi>p</mml:mi></mml:math>-body interacting systems in effective quantum 2-body Hamiltonians

Cataudella, V.; Hegde, Pratibha Raghupati; Lucignano, P.; Passarelli, Gianluca; Vitiello, Autilia

doi:10.1016/j.asoc.2021.107634

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…They can also be used to improve swarmbased and other population-based metaheuristics [30]. In addition, several types of local search strategies have been adopted, such as simulated annealing [31], tabu search [32], and pattern search [33], to improve the solution refinement capabilities of the swarmbased metaheuristics.…”

Section: Background Of Memetic Algorithmsmentioning

confidence: 99%

An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery

Song,

Fang

2023

Mathematics

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With the development of the e-commerce industry, using UAVs (unmanned aerial vehicles) to deliver goods has become more popular in transportation systems. This delivery method can reduce labor costs and improve the distribution efficiency, and UAVs can reach places that are difficult for humans to reach. Because some goods are perishable, the quality of the delivery will have an impact on the customer satisfaction. At the same time, the delivery time should also meet the needs of customers as much as possible. Therefore, this paper takes the distribution distance and customer satisfaction as the objective functions, establishes a bi-objective dynamic programming model, and proposes an improved SPEA2 (strength Pareto evolutionary algorithm 2). The improved algorithm introduces the local search strategy, on the basis of the original algorithm. It conducts a local search for the better non-dominated solutions obtained in each iteration. The new dominated solutions and non-dominated solutions are determined, and the crossover operator is improved, so that the local search ability is improved, on the basis of ensuring its global search ability. The numerical experiment results show that the improved algorithm achieves an excellent performance in three aspects: the Pareto front, generation distance, and spacing, and would have a high application value in UAV cargo delivery and other MOPs (multi-objective optimization problems). The average spacing value of the improved algorithm is more than 20% smaller than SPEA2 + SDE (strength Pareto evolution algorithm 2–shift-based density estimation), which is the second-best algorithm. In the comparison of the average generation distance value, this number reaches 30%.

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Section: Background Of Memetic Algorithmsmentioning

confidence: 99%

An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery

Song,

Fang

2023

Mathematics

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“…The significant similarity between highly suited strings can guide the search [94]. Memetic algorithms rapidly growing in the evolutionary computation studies area [95], [96], [97].…”

Section: ) Memetic Algorithm (Ma)mentioning

confidence: 99%

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

et al. 2022

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Feature selection is employed to reduce feature dimensions and computational complexity by eliminating irrelevant and redundant features. A vast amount of increasing data and its processing generate many feature sets, that are reduced by the feature selection process to improve the performance in all sorts of classification, regression, clustering models. This research performs a detailed analysis of motivation and concentrates on the fundamental architecture of feature selection. The study aims to establish a structured formation related to popular methods such as filter, wrapper, embedded into search strategies, evaluation criteria, and learning methods. Different methods organize a comparison of benefits and drawbacks followed by multiple classification algorithms and standard validation measures. The diversity of applications in multiple domains such as data retrieval, prediction analysis, and medical, intrusion, and industrial applications are efficiently highlighted. The study focused on some additional feature selection methods for handling big data. Nonetheless, new challenges have surfaced in the analysis of such data, which are also addressed in this study. Reflecting on commonly encountered challenges and clarifying how to obtain the absolute feature selection method are the significant components of this study.

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“…One of the main routes to tackle this problem, in the unitary limit, is to engineer the annealing schedules as opposed to using the simple linear schedules. There have been many approaches to construct optimal schedules for the given problem in the recent past [58][59][60][61][62][63], and some of them [64] are based on genetic algorithms, that have been also fruitfully applied to the embedding problem [65][66][67][68][69] for QA.…”

Section: Introductionmentioning

confidence: 99%

Deep learning optimal quantum annealing schedules for random Ising models

Hegde

Passarelli²,

Cantele³

et al. 2023

New J. Phys.

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A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory (LSTM) neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training. 

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Memetic algorithms for mapping p-body interacting systems in effective quantum 2-body Hamiltonians

Cited by 9 publications

References 43 publications

An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery

An Improved Strength Pareto Evolutionary Algorithm 2 with Adaptive Crossover Operator for Bi-Objective Distributed Unmanned Aerial Vehicle Delivery

A Comprehensive Survey on the Process, Methods, Evaluation, and Challenges of Feature Selection

Deep learning optimal quantum annealing schedules for random Ising models

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