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 behaviour 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.

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In this work we introduce a multiagent architecture conceived as a conceptual and practical framework for metaheuristic algorithms (MAGMA, MultiAGent Metaheuristics Architecture). Metaheuristics can be seen as the result of the interaction among di erent kinds of agents: level 0 agents constructing initial solutions, level-1 agents improving solutions and level-2 agents providing the high level strategy. In this framework, classical metaheuristic algorithms can be smoothly accommodated and extended, and new algorithms can be easily designed by defining which agents are involved and their interactions. Furthermore, with the introduction of a fourth level of agents, coordinating lower level agents, MAGMA can also describe, in a uniform way, cooperative search and, in general, any combination of metaheuristics. We propose

Abstract. Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and classifies them based on various characteristics. In particular with respect to low-level hybrids of different metaheuristics, a unified view based on a common pool template is described. It helps in making similarities and different key components of existing metaheuristics explicit. We then consider these key components as a toolbox for building new, effective hybrid metaheuristics. This approach of thinking seems to be superior to sticking too strongly to the philosophies and historical backgrounds behind the different metaheuristic paradigms. Finally, particularly promising possibilities of combining metaheuristics with constraint programming and integer programming techniques are highlighted. 1

Genetic algorithms (GAs) have recently become very popular by solving combinatorial optimization problems. In this paper, we propose an extension of the hybrid genetic algorithm for the wellknown combinatorial optimization problem, the quadratic assignment problem (QAP). This extension is based on the “fast hybrid genetic algorithm ” concept. An enhanced tabu search is used in the role of the fast local improvement of solutions, whereas a robust reconstruction (mutation) strategy is responsible for maintaining a high degree of the diversity within the population. We tested our algorithm on the instances from the QAP instance library QAPLIB. The results demonstrate promising performance of the proposed algorithm.

In this work we investigate the effects of the parallelization of a local search algorithm for MAX-SAT. The variables of the problem are divided in subsets and local search is applied to each of them in parallel, supposing that variables belonging to other subsets remain unchanged. We show empirical evidence for the existence of a critical level of parallelism which leads to the best performance. This result allows to improve local search and adds new elements to the investigation of criticality and parallelism in combinatorial optimization problems.

Abstract—Differential evolution (DE) and particle swarm op-timization (PSO) are two formidable population-based optimiz-ers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, oper-ating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and ana-lyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other rep-resentative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify dif-ferent hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploita-tion regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future. Index Terms—Differential evolution (DE), evolutionary opti-mization, exploration and exploitation, hybridization strategies, memetic algorithms (MAs), particle swarm optimization (PSO), population-based metaheuristics, taxonomy. I.

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In this work we introduce the multiagent metaphor as a framework for metaheuristic algorithms. The first step is the definition of agents which search on a fitness landscape. In this framework it is possible to implement the classical metaheuristic algorithms and introduce cooperative search in a well-defined way. The second step extends the model in a multi-level system, where agents at each level have different computational capabilities and tasks: solution construction, solution improvement, extracting solutions building blocks, analyzing regions of the search space and perform meta-reasoning on the behavior of lower level agents. We propose this perspective with the aim to achieve a better and clearer understanding of metaheuristics, obtain new algorithms and suggest directions for a software engineering-oriented implementation.

Abstract. A few attempts to create taxonomies in evolutionary computation have been made. These either group algorithms or group problems on the basis of their similarities. Similarity is typically evaluated by manually analysing algorithms/problems to identify key characteristics that are then used as a basis to form the groups of a taxonomy. This task is not only very tedious but it is also rather subjective. As a consequence the resulting taxonomies lack universality and are sometimes even questionable. In this paper we present a new and powerful approach to the construction of taxonomies and we apply it to Genetic Programming (GP). Only one manually constructed taxonomy of problems has been proposed in GP before, while no GP algorithm taxonomy has ever been suggested. Our approach is entirely automated and objective. We apply it to the problem of grouping GP systems with their associated parameter settings. We do this on the basis of performance signatures which represent the behaviour of each system across a class of problems. These signatures are obtained thorough a process which involves the instantiation of models of GP’s performance. We test the method on a large class of Boolean induction problems. 1