Andrea Roli

Metaheuristics in combinatorial optimization: Overview and conceptual comparison

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

Hybrid metaheuristics in combinatorial optimization: A survey

Research in metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving the problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization. This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different metaheuristics but includes, for example, the combination of exact algorithms and metaheuristics. In this work we provide a survey of some of the most important lines of hybridization. The literature review is accompanied by the presentation of illustrative examples.

MAGMA: a multiagent architecture for metaheuristics

In this work, we introduce a multiagent architecture called the MultiAGent Metaheuristic Architecture (MAGMA) conceived as a conceptual and practical framework for metaheuristic algorithms. Metaheuristics can be seen as the result of the interaction among different kinds of agents: The basic architecture contains three levels, each hosting one or more agents. Level-0 agents build solutions, level-1 agents improve solutions, and level-2 agents provide the high level strategy. In this framework, classical metaheuristic algorithms can be smoothly accommodated and extended. The basic three level architecture can be enhanced with the introduction of a fourth level of agents (level-3 agents) coordinating lower level agents. With this additional level, MAGMA can also describe, in a uniform way, cooperative search and, in general, any combination of metaheuristics. We describe the entire architecture, the structure of agents in each level in terms of tuples, and the structure of their coordination as a labeled transition system. We propose this perspective with the aim to achieve a better and clearer understanding of metaheuristics, obtain hybrid algorithms, suggest guidelines for a software engineering-oriented implementation and for didactic purposes. Some specializations of the general architecture will be provided in order to show that existing metaheuristics [e.g., greedy randomized adaptive procedure (GRASP), ant colony optimization (ACO), iterated local search (ILS), memetic algorithms (MAs)] can be easily described in our framework. We describe cooperative search and large neighborhood search (LNS) in the proposed framework exploiting level-3 agents. We show also that a simple hybrid algorithm, called guided restart ILS, can be easily conceived as a combination of existing components in our framework.

Emergence and control of macro-spatial structures in perturbed cellular automata, and implications for pervasive computing systems

Predicting the behavior of complex decentralized pervasive computing systems before their deployment in a dynamic environment, as well as being able to influence and control their behavior in a decentralized way, will be of fundamental importance in the near future. In this context, this paper describes the general behavior observed in a large set of asynchronous cellular automata when external perturbations influence the internal activities of cellular automata cells. In particular, we observed that stable macrolevel spatial structures emerge from local interactions among cells, a behavior that does not emerge when cellular automata are not perturbed. Similar sorts of macrolevel behaviors are likely to emerge in the context of pervasive computing systems and need to be studied, controlled, and possibly fruitfully exploited. On this basis, the paper also reports the results of a set of experiments, showing how it is possible to control, in a decentralized way, the behavior of perturbed cellular automata, to make any desired patterns emerge.

Task partitioning in swarms of robots: an adaptive method for strategy selection

Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals’ skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.

Hybrid metaheuristics for constrained portfolio selection problems

Portfolio selection is a problem arising in finance and economics. While its basic formulations can be efficiently solved using linear or quadratic programming, its more practical and realistic variants, which include various kinds of constraints and objectives, have in many cases to be tackled by heuristics. In this work, we present a hybrid technique that combines a local search metaheuristic, as master solver, with a quadratic programming procedure, as slave solver. Experimental results show that the approach is very promising, as it regularly provides the optimal solution and thus achieves results comparable, or superior, to state-of-the-art solvers, including widespread commercial software tools (CPLEX 11.0.1 and MOSEK 5). The paper reports a detailed analysis of the behavior of the technique in various constraint settings, thus demonstrating how the performance is dependent on the features of the instance.

Symmetry breaking and local search spaces

The effects of combining search and modelling techniques can be complex and unpredictable, so guidelines are very important for the design and development of effective and robust solvers and models. A recently observed phenomenon is the negative effect of symmetry breaking constraints on local search performance. The reasons for this are poorly understood, and we attempt to shed light on the phenomenon by testing three conjectures: that the constraints create deep new local optima; that they can reduce the relative size of the basins of attraction of global optima; and that complex local search heuristics reduce their negative effects.

A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP

The population-based ant colony optimization algorithm (P-ACO) uses a very different pheromone update when compared to other ACO algorithms. In this work, we study P-ACOs behavior for solving the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results clearly show that P-ACO is a very competitive tool whose parameters and behavior depend strongly on the problem tackled and on whether a local search is used.

The detection of intermediate-level emergent structures and patterns.

Artificial life is largely concerned with systems that exhibit different emergent phenomena; yet, the identification of emergent structures is frequently a difficult challenge. In this paper we introduced a system to identify candidate emergent mesolevel dynamical structures in dynamical networks. This method is based on an extension of a measure introduced for detecting clusters in biological neural networks; its main novelty in comparison to previous application of similar measures is that we used it to consider truly dynamical networks, and not only fluctuations around stable asymptotic states. The identified structures are clusters of elements that behave in a coherent and coordinated way and that loosely interact with the remainder of the system. We have evidence that our approach is able to identify these “emerging things” in some artificial network models and in more complex data coming from catalytic reaction networks and biological gene regulatory systems (A.thaliana). We think that this system could suggest interesting new ways in dealing with artificial and biological systems.

Hybrid Local Search for Constrained Financial Portfolio Selection Problems

Portfolio selection is a relevant problem arising in finance and economics. While its basic formulations can be efficiently solved through linear or quadratic programming, its more practical and realistic variants, which include various kinds of constraints and objectives, have in many cases to be tackled by approximate algorithms. In this work, we present a hybrid technique that combines a local search, as mastersolver, with a quadratic programming procedure, as slavesolver. Experimental results show that the approach is very promising and achieves results comparable with, or superior to, the state of the art solvers.