Titlebook: Metaheuristics; 14th International C Luca Di Gaspero,Paola Festa,Mario Pavone Conference proceedings 2023 The Editor(s) (if applicable) and

尊重

,Tabu Search with?Multiple Decision Levels for?Solving Heterogeneous Fleet Pollution Routing Problemods. Thus, they face significant challenges in managing their orders to be delivered on time. However, transportation is responsible for . of the . emissions of the total polluting gases in the atmosphere. Therefore, there is a growing interest to investigate methods to optimize logistics and to con

Hyaluronic-Acid

,A Learning Metaheuristic Algorithm for?a?Scheduling Application,ies to improve the performance of metaheuristics have increasingly relied on the use of various methods, either combining different metaheuristics or originating outside of the metaheuristic field. This paper presents a learning algorithm to improve the performance of tabu search by reducing its sea

廢止

,MineReduce-Based Metaheuristic for?the?Minimum Latency Problem,at vertices. Recently, a proposal that incorporates data mining into a state-of-the-art metaheuristic by injecting patterns from high-quality solutions has consistently led to improved results in terms of solution quality and running time for this problem. This paper extends that proposal by leverag

SLUMP

Optimizing Multi-variable Time Series Forecasting Using Metaheuristics,eal-world behavior is useful for understanding the intrinsic relationship between variables. However, selecting a predictor that ensures good performance for variables of different natures is not always a simple process. An algorithmic approach based on metaheuristics could be a good alternative to

離開

,Unsupervised Machine Learning for?the?Quadratic Assignment Problem, local search algorithms are proposed. The extraction of frequent itemsets in the context of local search is shown to produce good results for a few problem instances. Negative results of the proposed learning mechanism are reported for other instances. This result contrasts with other hard optimiza

燒烤

BATE

Neural Architecture Search Using Differential Evolution in MAML Framework for Few-Shot Classificatietwork that can then be adapted to any novel task using a few optimization steps. In this study, we take MAML with a simple four-block convolution architecture as our baseline, and try to improve its few-shot classification performance by using an architecture generated automatically through the neu

debunk

Neural Architecture Search Using Metaheuristics for Automated Cell Segmentation,gnificant limitation hampering the effectiveness of deep neural network based segmentation task. Manual selection of these hyper-parameters is not feasible as the search space increases. At the same time, these generated networks are problem-specific. Recently, studies that perform segmentation of m

COKE

,Analytical Methods to?Separately Evaluate Convergence and?Diversity for?Multi-objective Optimizationvergence and diversity for quantitatively comparing MOEAs. Specifically, Convergence-Diversity Pair (C-D Pair) is proposed to statistically compare the convergence and diversity of two MOEAs. C-D Pair provides analytical information on the overall experimental results. In addition, Convergence-Dive

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