An Improved Excitation Matching Method based on an Ant Colony Optimization for Suboptimal-Free Clustering in Sum-Difference Compromise Synthesis

Dealing with an excitation matching method, this paper presents a global optimization strategy for the optimal clustering in sum-difference compromise linear arrays. Starting from a combinatorial formulation of the problem at hand, the proposed technique is aimed at determining the sub-array configuration expressed as the optimal path inside a directed acyclic graph structure modelling the solution space. Towards this end, an ant colony metaheuristic is used to benefit of its hill-climbing properties in dealing with the non-convexity of the sub-arraying as well as in managing graph searches. A selected set of numerical experiments are reported to assess the efficiency and current limitations of the ant-based strategy also in comparison with previous local combinatorial search methods. (c) 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

A multidirectional modified Physarum solver for discrete decision making

In this paper, a bio-inspired algorithm able to incrementally grow decision graphs in multiple directions is presented. The heuristic draws inspiration from the behaviour of the slime mould Physarum Polycephalum. In its main vegetative state, the plasmodium, this large single-celled amoeboid organism extends and optimizes a net of veins looking for food. The algorithm is here used to solve classical problems in operations research (symmetric Traveling Salesman and Vehicle Routing Problems). Simulations on selected test cases demonstrate that a multidirectional modied Physarum solver performs better than a unidirectional one. The ability to evaluate decisions from multiple directions enhances the performance of the solver in the construction and selection of optimal decision sequences

The edge-disjoint path problem on random graphs by message-passing

We present a message-passing algorithm to solve the edge disjoint path problem (EDP) on graphs incorporating under a unique framework both traffic optimization and path length minimization. The min-sum equations for this problem present an exponential computational cost in the number of paths. To overcome this obstacle we propose an efficient implementation by mapping the equations onto a weighted combinatorial matching problem over an auxiliary graph. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behaviour of both the number of paths to be accommodated and their minimum total length.Comment: 14 pages, 8 figure

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Sensor networks security based on sensitive robots agents. A conceptual model

Multi-agent systems are currently applied to solve complex problems. The security of networks is an eloquent example of a complex and difficult problem. A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion Detection is introduced in the current paper. The proposed technique could be used with machine learning based intrusion detection techniques. The new model uses the reaction of virtual sensitive robots to different stigmergic variables in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page

A memetic algorithm for the university course timetabling problem

This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that must be respected and a huge search space to be explored, even if the size of the problem input is not significantly large. On the other hand, the problem itself does not have a widely approved definition, since different institutions face different variations of the problem. This paper presents a memetic algorithm that integrates two local search methods into the genetic algorithm for solving the university course timetabling problem (UCTP). These two local search methods use their exploitive search ability to improve the explorative search ability of genetic algorithms. The experimental results indicate that the proposed memetic algorithm is efficient for solving the UCTP