Search results for "Optimization problem"
showing 10 items of 281 documents
Clustering local tourism systems by threshold acceptance
2015
Despite the importance of tourism as a leading industry in the development of a country’s economy, there is a lack of criteria and methodologies for the detection, promotion and governance of local tourism systems. We propose a quantitative approach for the detection of local tourism systems that are optimal with respect to geographical, economic, and demographical criteria. To this end, we formulate the issue as an optimization problem, and we solve it by means of Threshold Acceptance, a meta-heuristic algorithm which does not require us to predefine the number of clusters and also does not require all geographic areas to belong to a cluster.
Experiments on Concurrent Artificial Environment
2001
We show how the simulation of concurrent system is of interest for both behavioral studies and strategies of learning applied on prey-predator problems. In our case learning studies into unknown environment have been applied to mobile units by using genetic algorithms (GA). A set of trajectories, generated by GA, are able to build a description of the external scene driving a predators to a prey. Here, an example of prey-predator strategy,based on field of forces, is proposed. The evolution of the corespondent system can be formalized as an optimization problem and, for that purpose, GA can be use to give the right solution at this problem. This approach could be applied to the autonomous r…
Newton Method for Minimal Learning Machine
2021
Minimal Learning Machine (MLM) is a distance-based supervised machine learning method for classification and regression problems. Its main advances are simple formulation and fast learning. Computing the MLM prediction in regression requires a solution to the optimization problem, which is determined by the input and output distance matrix mappings. In this paper, we propose to use the Newton method for solving this optimization problem in multi-output regression and compare the performance of this algorithm with the most popular Levenberg–Marquardt method. According to our knowledge, MLM has not been previously studied in the context of multi-output regression in the literature. In additio…
Integer linear programming in computational biology
2009
Computational molecular biology (bioinformatics) is a young research field that is rich in NP-hard optimization problems. The problem instances encountered are often huge and comprise thousands of variables. Since their introduction into the field of bioinformatics in 1997, integer linear programming (ILP) techniques have been successfully applied to many optimization problems. These approaches have added much momentum to development and progress in related areas. In particular, ILP-based approaches have become a standard optimization technique in bioinformatics. In this review, we present applications of ILP-based techniques developed by members and former members of Kurt Mehlhorn's group.…
Integrating Cross-Dominance Adaptation in Multi-objective Memetic Algorithms
2008
This chapter proposes a novel adaptive memetic approach for solving multi-objective optimization problems. The proposed approach introduces the novel concept of crossdominance and employs this concept within a novel probabilistic scheme which makes use of the Wigner distribution for performing coordination of the local search. Thus, two local searchers are integrated within an evolutionary framework which resorts to an evolutionary algorithm previously proposed in literature for solving multi-objective problems. These two local searchers are a multi-objective version of simulated annealing and a novel multi-objective implementation of the Rosenbrock algorithm.
Guaranteed lower bounds for cost functionals of time-periodic parabolic optimization problems
2019
In this paper, a new technique is shown for deriving computable, guaranteed lower bounds of functional type (minorants) for two different cost functionals subject to a parabolic time-periodic boundary value problem. Together with previous results on upper bounds (majorants) for one of the cost functionals, both minorants and majorants lead to two-sided estimates of functional type for the optimal control problem. Both upper and lower bounds are derived for the second new cost functional subject to the same parabolic PDE-constraints, but where the target is a desired gradient. The time-periodic optimal control problems are discretized by the multiharmonic finite element method leading to lar…
A gradient-based decomposition approach to optimize pressure path and counterpunch action in Y-shaped tube hydroforming operations
2008
International audience; In tube hydroforming, the concurrent actions of pressurized fluid and mechanical feeding allows obtaining tube shapes characterized by complex geometries such as different diameters sections and/or bulged zones. Main process parameters are material feeding history (i.e., the punches velocity history), internal pressure path during the process, and (in T- or Y-shaped tube hydroforming) counterpunch action. What is crucial, in such processes, is the proper design of operative parameters aimed to avoid defects (for instance underfilling or ductile fractures). Actually, the design of tube hydroforming operations is mainly aimed to prevent bursting or buckling occurrence …
The potential of optimization in communal routing problems: case studies from Finland
2009
Abstract: In many European countries, municipalities offer their inhabitants a wide variety of social services. In this paper we will focus on efficiently scheduling home care, transportation of the elderly, and home meal delivery. These so-called municipal or communal routing problems can be modeled as different variants of the vehicle routing problem, a well-known optimization problem from the literature. We present a focused literature review and report on case studies using Finnish data. The computational results show that there is a significant potential for cost savings for all applications considered.
Greedy and K-Greedy algoritmhs for multidimensional data association
2011
[EN] The multidimensional assignment (MDA) problem is a combinatorial optimization problem arising in many applications, for instance multitarget tracking (MTT). The objective of an MDA problem of dimension $d\in\Bbb{N}$ is to match groups of $d$ objects in such a way that each measurement is associated with at most one track and each track is associated with at most one measurement from each list, optimizing a certain objective function. It is well known that the MDA problem is NP-hard for $d\geq3$. In this paper five new polynomial time heuristics to solve the MDA problem arising in MTT are presented. They are all based on the semi-greedy approach introduced in earlier research. Experimen…
Water quality sensor placement: a multi-objective and multi-criteria approach
2021
[EN] To satisfy their main goal, namely providing quality water to consumers, water distribution networks (WDNs) need to be suitably monitored. Only well designed and reliable monitoring data enables WDN managers to make sound decisions on their systems. In this belief, water utilities worldwide have invested in monitoring and data acquisition systems. However, good monitoring needs optimal sensor placement and presents a multi-objective problem where cost and quality are conflicting objectives (among others). In this paper, we address the solution to this multi-objective problem by integrating quality simulations using EPANET-MSX, with two optimization techniques. First, multi-objective op…