No abstract

No abstract

This paper introduces a method for the distribution of any and all population-based metaheuristics. It improves on the naive approach, independent multiple runs, while adding negligible overhead. Existing methods that coordinate instances across a cluster typically require some compromise of more complex design, higher communication loads, and solution propagation rate, requiring more work to develop and more resources to run. The aim of the new method is not to achieve state-of-the-art results, but rather to provide a better baseline method than multiple independent runs. The main concept of the method is that one of the instances receives updates with the current best solution of all other instances. This work describes the general approach and its particularization to both genetic algorithms and ant colony optimization for solving Traveling Salesman Problems (TSPs). It also includes extensive tests on the TSPLIB benchmark problems of resulting quality of the solutions and anytime performance (solution quality versus time to reach it). These tests show that the new method yields better solutions for about two thirds of the problems and equivalent solutions in the remaining third, and consistently exhibits better anytime performance.

In a site with one set of blast furnaces that must feed two steel mills separated by over 13 kilometers, allocation of hot pig iron is a complex subject owing to the long cycle times and the large number of parameters involved in the decision making. Simulation of the transportation appears as an ideal solution to tackle this complexity. However, allocation priorities are stategic issues that change in time, as does, if less often, the available railway layout.A simulation based decision support system has been developed to integrate in the current torpedo tracking application that works around these difficulties by generating the simulations dynamically from three sources: a database model of the transportation network, the dynamic database that holds the current state of the torpedo wagons (through the tracking application), and a shared, updatable knowledge base made up of several sets of rules and priorities, and conditional paths of ruleset selection. The knowledge base is now being built on expert knowledge and current strategies, and will provide a framework for user-accessible update. KEYWORDSKnowledge-based simulation, decision support systems, applied simulation.

We propose an evolutionary algorithm for the enhancement of digital semi-fragile watermaking based on the manipulation of the image discrete cosine transform (DCT). The algorithm searches for the optimal localization of the DCT of an image to place the mark image DCT coefficients. The problem is stated as a multi-objective optimization problem (MOP), that involves the simultaneous minimization of distortion and robustness criteria.