The Steiner tree problem (STP) is one of the most popular combinatorial optimiza-tion problems with various practical applications. In this paper, we propose a Break-out Local Search (BLS) algorithm for an important generalization of the STP: the Steiner tree problem with revenue, budget and hop constraints (STPRBH), which consists of determining a subtree of a given undirected graph which maximizes the collected revenues, subject to both budget and hop constraints. Starting from a probabilistically constructed initial solution, BLS uses a Neighborhood Search (NS) procedure based on several specifically designed move operators for local optimiza-tion, and employs an adaptive diversification strategy to escape from local optima. The diversification mechanism is implemented by adaptive perturbations, guided by dedicated information of discovered high-quality solutions. Computational results based on 240 benchmarks show that BLS produces competitive results with respect to several previous approaches. For the 56 most challenging instances with unknown optimal results, BLS succeeds in improving 49 and matching one best known results within reasonable time. For the 184 instances which have been solved to optimality, BLS can also match 167 optimal results.

In this paper, we propose the first heuristic approach for the vertex separator problem (VSP), based on Breakout Local Search (BLS). BLS is a recent meta-heuristic that follows the general framework of the popular Iterated Local Search (ILS) with a particular focus on the perturbation strategy. Based on some relevant information on search history, it tries to introduce the most suitable degree of diversification by determining adaptively the number and type of moves for the next perturbation phase. The proposed heuristic is highly competitive with the exact state-of-art approaches from the literature on the current VSP benchmark. Moreover, we present for the first time computational results for a set of large graphs with up to 3000 vertices, which constitutes a new challenging benchmark for VSP approaches.

The quadratic assignment problem (QAP) is one of the most studied NP-hard problems with various practical applications. In this work, we propose a powerful population-based memetic algorithm (called BMA) for QAP. BMA in-tegrates an effective local optimization algorithm called Breakout Local Search (BLS) within the evolutionary computing framework which itself is based on a uniform crossover, a fitness-based pool updating strategy and an adaptive mu-tation procedure. Extensive computational studies on the set of 135 well-known benchmark instances from the QAPLIB revealed that the proposed algorithm is able to attain the best-known results for 133 instances and thus competes very favorably with the current most effective QAP approaches. A study of the search landscape and crossover operators is also proposed to shed light on the behavior of the algorithm.

Abstract Keyword search over XML data has attracted a lot of research efforts in the last decade, where one of the fundamental research problems is how to efficiently answer a given keyword query w.r.t. a certain query semantics. We found that the key factor resulting in the inefficiency for existing methods is that they all heavily suffer from the common ancestor repetition (CAR) problem. In this paper, we propose a novel form of inverted list, namely the IDList; the IDList for keyword k consists of ordered nodes that directly or indirectly contain k. We then show that finding keyword query results based on the SLCA and ELCA semantics can be reduced to ordered set intersection problem, which has been heavily optimized due to its application in areas such as information retrieval and database systems. We propose several algorithms that exploit set intersection in different directions and with or without using additional indexes. We further propose several algorithms that are based on hash search to simplify the operation of finding common nodes from all involved IDLists. We have conducted an extensive set of experiments using many stateof-the-art algorithms and several large-scale datasets. The results demonstrate that our proposed methods outperform existing methods by up to two orders of magnitude in many cases.

Abstract. We investigate the contribution of a recently proposed adaptive diversification strategy (ADS) to the performance of an iterated local search (ILS) algorithm. ADS is used as a diversification mechanism by breakout local search (BLS), which is a new variant of the ILS metaheuristic. The proposed perturbation strategy adaptively selects between two types of perturbations (directed or random moves) of different intensities, depending on the current state of search. We experimentally evaluate the performance of ADS on the quadratic assignment problem (QAP) and the maximum clique problem (MAX-CLQ). Computational results accentuate the benefit of combining adaptively multiple perturbation types of different intensities. Moreover, we provide some guidance on when to introduce a weaker and when to introduce a stronger diversification into the search.

Given an undirected graph with costs associated with each edge as well as each pair of edges, the quadratic minimum spanning tree problem (QMSTP) consists of determining a spanning tree of minimum cost. QMSTP is useful to model many real-life network design applications. We propose a three-phase search approach named TPS for solving QMSTP, which organizes the search process into three distinctive phases which are iterated: 1) a descent neighborhood search phase using two move operators to reach a local optimum from a given starting solution, 2) a local optima exploring phase to discover nearby local optima within a given regional area, and 3) a perturbation-based diversication phase to jump out of the current regional search area. TPS also introduces a pre-estimation criterion to signicantly improve the efficiency of neighborhood evaluation, and develops a new swap-vertex neighbor-hood (as well as a swap-vertex based perturbation operator) which prove to be quite powerful for solving a series of special instances with particular structures. Com-putational experiments based on 7 sets of 659 popular benchmarks show that TPS produces highly competitive results compared to the best performing approaches in the literature. TPS discovers improved best known results (new upper bounds) for 33 open instances and matches the best known results for all the remaining instances. Critical elements and parameters of the TPS algorithm are analyzed to understand its behavior.

Inspired by the idea of interacting intelligent agents of a multi-agent system, we introduce a multi-agent based optimization method applied to the quadratic assignment problem (MAOM-QAP). MAOM-QAP is composed of several agents (decision-maker agent, local search agents, crossover agents and perturbation agent) which are designed for the purpose of intensied and diversied search activities. With the help of a reinforcement learning mechanism, MAOM-QAP dynamically decides the most suitable agent to activate according to the state of search process. Under the coordination of the decision-maker agent, the other agents fulll dedicated search tasks. The performance of the proposed approach is assessed on the set of well-known QAP benchmark instances, and compared with the most advanced QAP methods of the literature. The ideas proposed in this work are rather general and could be adapted to other optimization tasks. This work opens the way for designing new distributed intelligent systems for tackling other complex search problems.

Support The quadratic assignment problem (QAP) is very challengeable and interesting problem that can model many real-life problems. In this paper, we will simply discuss the meaning of quadratic assignment problem, solving techniques and we will give a survey of some developments and researches.