A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue

Enhancing Parallel Cooperative Trajectory Based Metaheuristics with Path Relinking

This paper proposes a novel algorithm combining path relinking with a set of cooperating trajectory based parallel algorithms to yield a new metaheuristic of enhanced search features. Algorithms based on the exploration of the neighborhood of a single solution, like simulated annealing (SA), have offered accurate results for a large number of real-world problems in the past. Because of their trajectory based nature, some advanced models such as the cooperative one are competitive in academic problems, but still show many limitations in addressing large scale instances. In addition, the field of parallel models for trajectory methods has not deeply been studied yet (at least in comparison with parallel population based models). In this work, we propose a new hybrid algorithm which improves cooperative single solution techniques by using path relinking, allowing both to reduce the global execution time and to improve the efficacy of the method. We test here this new model using a large benchmark of instances of two well-known NP-hard problems: MAXSAT and QAP, with competitive results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Design of Multi-Objective Evolutionary Algorithms: Application to the Flow-Shop Scheduling Problem

International audienc

ParaDisEO-Based Design of Parallel and Distributed Evolutionary Algorithms

The original publication is available at www.springerlink.comInternational audienceParaDisEO is a framework dedicated to the design of parallel and distributed metaheuristics including local search methods and evolutionary algorithms. This paper focuses on the latter aspect. We present the three parallel and distributed models implemented in ParaDisEO and show how these can be exploited in a user-friendly, flexible and transparent way. These models can be deployed on distributed memory machines as well as on shared memory multi-processors, taking advantage of the shared memory in the latter case. In addition, we illustrate the instantiation of the models through two applications demonstrating the efficiency and robustness of the framework

An Agent-Based Approach to Self-Organized Production

The chapter describes the modeling of a material handling system with the production of individual units in a scheduled order. The units represent the agents in the model and are transported in the system which is abstracted as a directed graph. Since the hindrances of units on their path to the destination can lead to inefficiencies in the production, the blockages of units are to be reduced. Therefore, the units operate in the system by means of local interactions in the conveying elements and indirect interactions based on a measure of possible hindrances. If most of the units behave cooperatively ("socially"), the blockings in the system are reduced. A simulation based on the model shows the collective behavior of the units in the system. The transport processes in the simulation can be compared with the processes in a real plant, which gives conclusions about the consequencies for the production based on the superordinate planning.Comment: For related work see http://www.soms.ethz.c

Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign

Cross-selling campaigns seek to offer the right products to the set of customers with the goal of maximizing expected profit, while, at the same time, respecting the purchasing constraints set by investors. In this context, a bi-objective version of this NP-Hard problem is approached in this paper, aiming at maximizing both the promotion campaign total profit and the risk-adjusted return, which is estimated with the reward-to-variability ratio known as Sharpe ratio. Given the combinatorial nature of the problem and the large volume of data, heuristic methods are the most common used techniques. A Greedy Randomized Neighborhood Structure is also designed, including the characteristics of a neighborhood exploration strategy together with a Greedy Randomized Constructive technique, which is embedded in a multi-objective local search metaheuristic. The latter combines the power of neighborhood exploration by using a Pareto Local Search with Variable Neighborhood Search. Sets of non-dominated solutions obtained by the proposed method are described and analyzed for a number of problem instances

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Spatially Resolved Chemistry in Nearby Galaxies I. The Center of IC 342

We have imaged emission from the millimeter lines of eight molecules--C2H, C34S, N2H+, CH3OH, HNCO, HNC, HC3N, and SO--in the central half kpc of the nearby spiral galaxy IC 342. The 5" (~50 pc) resolution images were made with OVRO. Using these maps we obtain a picture of the chemistry within the nuclear region on the sizescales of individual GMCs. Bright emission is detected from all but SO. There are marked differences in morphology for the different molecules. A principal component analysis is performed to quantify similarities and differences among the images. This analysis reveals that while all molecules are to zeroth order correlated, that is, they are all found in dense molecular clouds, there are three distinct groups of molecules distinguished by the location of their emission within the nuclear region. N2H+, C18O, HNC and HCN are widespread and bright, good overall tracers of dense molecular gas. C2H and C34S, tracers of PDR chemistry, originate exclusively from the central 50-100 pc region, where radiation fields are high. The third group of molecules, CH3OH and HNCO, correlates well with the expected locations of bar-induced orbital shocks. The good correlation of HNCO with the established shock tracer molecule CH3OH is evidence that this molecule, whose chemistry has been uncertain, is indeed produced by processing of grains. HC3N is observed to correlate tightly with 3mm continuum emission, demonstrating that the young starbursts are the sites of the warmest and densest molecular gas. We compare our HNC images with the HCN images of Downes et al. (1992) to produce the first high resolution, extragalactic HCN/HNC map: the HNC/HCN ratio is near unity across the nucleus and the correlation of both of these gas tracers with the star formation is excellent. (Abridged).Comment: 54 pages including 10 figures and 8 tables. Accepted for publication in Ap

A bi‐objective procedure to deliver actionable knowledge in sport services

The increase in retention of customer in gyms and health clubs is nowadays a challenge that requires concrete and personalized actions. Traditional data mining studies focused essentially on predictive analytics, neglecting the business domain. This work presents an actionable knowledge discovery system which uses the following pipeline (data collection, predictive model, retention interventions). In the first step, it extracts and transforms existing real data from databases of the sports facilities. In a second step, predictive models are applied to identify user profiles more susceptible to dropout, where actionable withdrawal rules are based on actionable attributes. Finally, in the third step, based on the previous actionable knowledge some of the values of the actionable attributes should be changed in order to increase retention. Simulation of scenarios is carried out, with test and control groups, where business utility and associate cost are measured. This document presents a bi-objective study in order to choose the more efficient scenarios.info:eu-repo/semantics/publishedVersio