4,465 research outputs found
A statistical learning based approach for parameter fine-tuning of metaheuristics
Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version
A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model
We present a simple transformation of the formulation of the log-periodic
power law formula of the Johansen-Ledoit-Sornette model of financial bubbles
that reduces it to a function of only three nonlinear parameters. The
transformation significantly decreases the complexity of the fitting procedure
and improves its stability tremendously because the modified cost function is
now characterized by good smooth properties with in general a single minimum in
the case where the model is appropriate to the empirical data. We complement
the approach with an additional subordination procedure that slaves two of the
nonlinear parameters to what can be considered to be the most crucial nonlinear
parameter, the critical time defined as the end of the bubble and the
most probably time for a crash to occur. This further decreases the complexity
of the search and provides an intuitive representation of the results of the
calibration. With our proposed methodology, metaheuristic searches are not
longer necessary and one can resort solely to rigorous controlled local search
algorithms, leading to dramatic increase in efficiency. Empirical tests on the
Shanghai Composite index (SSE) from January 2007 to March 2008 illustrate our
findings
A Framework for Genetic Algorithms Based on Hadoop
Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in
many real-world applications. The sequential execution of GAs requires
considerable computational power both in time and resources. Nevertheless, GAs
are naturally parallel and accessing a parallel platform such as Cloud is easy
and cheap. Apache Hadoop is one of the common services that can be used for
parallel applications. However, using Hadoop to develop a parallel version of
GAs is not simple without facing its inner workings. Even though some
sequential frameworks for GAs already exist, there is no framework supporting
the development of GA applications that can be executed in parallel. In this
paper is described a framework for parallel GAs on the Hadoop platform,
following the paradigm of MapReduce. The main purpose of this framework is to
allow the user to focus on the aspects of GA that are specific to the problem
to be addressed, being sure that this task is going to be correctly executed on
the Cloud with a good performance. The framework has been also exploited to
develop an application for Feature Subset Selection problem. A preliminary
analysis of the performance of the developed GA application has been performed
using three datasets and shown very promising performance
Ant colony optimization and its application to the vehicle routing problem with pickups and deliveries
Ant Colony Optimization (ACO) is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. It was first introduced for solving the Traveling Salesperson Problem. Since then many implementations of ACO have been proposed for a variety of combinatorial optimization. In this chapter, ACO is applied to the Vehicle Routing Problem with Pickup and Delivery (VRPPD). VRPPD determines a set of vehicle routes originating and ending at a single depot and visiting all customers exactly once. The vehicles are not only required to deliver goods but also to pick up some goods from the customers. The objective is to minimize the total distance traversed. The chapter first provides an overview of ACO approach and presents several implementations to various combinatorial optimization problems. Next, VRPPD is described and the related literature is reviewed, Then, an ACO approach for VRPPD is discussed. The approach proposes a new visibility function which attempts to capture the “delivery” and “pickup” nature of the problem. The performance of the approach is tested using well-known benchmark problems from the literature
- …