Unsupervised kernel regression (UKR), the unsupervised counterpart of the Nadaraya-Watson estimator, is a dimension reduction technique for learning of low-dimensional manifolds. It is based on optimizing representative low-dimensional latent variables with regard to the data space reconstruction error. The problem of scaling initial local linear embedding solutions, and optimization in latent ...

Local search (LS) methods heuristically find a solution for constraint satisfaction problems (CSP). LS starts the search for a solution from a random assignment. LS then examines the neighbours of this assignment to determine a better neighbour valuation to move to. It repeats this process of moving from the current assignment to a better assignment until it finds a solution that satisfies all ...

Nodifferencewas detected on the performances of construction heuristics for TSP developed in Task 1 between Euclidean instances with points uniformly sparse and Euclidean instances with points clustered. Attention was given to the choice of data structures to represent and maintain a solution during a perturbative search. We described Tabu Search (TS) and its variations: robust and reactive. We...

This paper proposes an Iterated Local Search (ILS) procedure and an Iterated Greedy (IG) algorithm, which are both combined with a variable neighbourhood search (VNS), for dealing with the flow shop problem with blocking, in order to minimize the total tardiness of jobs. The structure of both algorithms is very similar, but they differ in the way that the search is diversified in the space of s...

This paper deals with the Heterogeneous Fleet Vehicle Routing Problem (HFVRP). The HFVRP is N P-hard since it is a generalization of the classical Vehicle Routing Problem (VRP), in which clients are served by a heterogeneous fleet of vehicles with distinct capacities and costs. The objective is to design a set of routes in such a way that the sum of the costs is minimized. The proposed algorith...

Since minimum sum-of-squares clustering (MSSC) is an NP hard combinatorial optimization problem, applying techniques from global optimization appears to be promising for reliably clustering numerical data. In this paper, concepts of combinatorial heuristic optimization are considered for approaching the MSSC: An iterated local search (ILS) approach is proposed which is capable of finding (near-...

Resumo: Neste trabalho, aborda-se o problema de roteamento de veículos com janelas de tempo e múltiplos entregadores, uma variante do problema de roteamento de veículos que, além das decisões de programação e roteamento dos veículos, envolve a determinação do tamanho da tripulação de cada veículo de entrega. Esse problema surge na distribuição de bens em centros urbanos congestionados em que, d...

Iterated Local Search (ILS) is a powerful framework for developing efficient algorithms for the Permutation Flow Shop Problem (PFSP). These algorithms are relatively simple to implement and use very few parameters, which facilitates the associated fine-tuning process. Therefore, they constitute an attractive solution for real-life applications. In this paper, we discuss some parallelization, pa...

The Cell Formation Problem is an NP-hard optimization problem that consists of grouping machines into cells dedicated to producing a family of product parts, so that each cell operates independently and inter-cellular movements are minimized. Due to its high computational complexity, several heuristic methods have been developed over the last decades. Hybrid methods based on adaptations of popu...

We revisit Additive Quantization (AQ), an approach to vector quantization that uses multiple, full-dimensional, and non-orthogonal codebooks. Despite its elegant and simple formulation, AQ has failed to achieve state-of-the-art performance on standard retrieval benchmarks, because the encoding problem, which amounts to MAP inference in multiple fully-connected Markov Random Fields (MRFs), has p...