IM-GSO: A Community Directed Group Search Optimization Approach for Influence Maximization

Abstract

Influence maximization is a fundamental problem in the study of complex relationship networks, such as viral marketing in business application areas. It is directed towards extracting a minimal (or k-sized) subset of most influential nodes with largest cascading effect across the network as per seeding budget. The problem is categorized as NP hard and hence greedy/heuristic techniques are extensively studied in the literature for generating reasonably acceptable solutions. This article proposes a novel nature based heuristic optimization algorithm IM-GSO to dynamically evolve near to optimal K-sized influential seed nodes for varied structural real world networks. IM-GSO smartly incorporates hidden structural patterns like communities, node degrees, betweenness and similarities for efficient candidate population generation. This smartly initialized population is then evolved using a discrete adaption of Group Search Optimization (GSO) algorithm. Correctness of IM-GSO is verified by optimizing two prominent spread estimation functions SIMPATH and MAGA, on varied sized (small/medium/large) networks. Detailed experimental evaluations by execution of 10,000 Monte Carlo simulations under Information Cascade (IC) model indicates a significantly high influence spread for IM-GSO seeds in contrast to standard heuristics techniques.     

Incremental Beam search

Beam search is a heuristic search algorithm that explores a state-space graph by expanding w most promising nodes at each level (depth) of the graph, where w is called the beam-width which is taken as input from the user. The quality of the solution produced by beam search does not always monotonically improve with the increase in beam-width making it difficult to choose an appropriate beam-width for effective use. We present an algorithm called Incremental Beam Search (IncB) which guarantees monotonicity, and is also anytime in nature. Experimental results on the sliding-tile puzzle, the traveling salesman, and the single-machine scheduling problems show that IncB significantly outperforms basic monotonic methods such as iterative widening beam search as well as some of the state-of-the-art anytime heuristic search algorithms in terms of the quality of the solution produced at the end as well as the anytime performance.     

On the scalability of expanding ring search for dense wireless sensor networks

Expanding Ring Search (ERS) is a prominent technique used for information discovery in multi-hop networks where the initiator of the search is unaware of any of the γ$\gamma$ locations of the target information. ERS reduces the overhead of the search by successively searching for a larger number of hops starting from the location of search initiator. Even though ERS reduces the overhead of the search compared to flooding, it still incurs a very high cost which makes it unsuitable especially for energy constrained networks like Wireless Sensor Networks (WSNs). Moreover, the cost of search (number of transmitted bytes) using ERS increases with node density, which limits its scalability in densely deployed WSNs. In this paper, we apply the principles of area coverage to ERS and propose a new protocol called Coverage based Expanding Ring Search (CERS) for energy efficient and scalable search in WSNs. CERS is configurable in terms of energy–latency trade-off which enables it applicable to varied application scenarios. The basic principle of CERS is to route the search packet along a set of ring based trajectories that minimizes the number of messages transmitted to find the target information. The transmissions are performed such that only a subset of total sensor nodes transmit the search packet to cover the entire terrain area while others listen. We believe that query resolution based on the principles of area coverage provides a new dimension for conquering the scale of dense WSNs. We compare CERS with existing query resolution techniques for unknown target location such as ERS, Random walk search, and Gossip search. We prove by both analysis and simulation that CERS is highly scalable, the cost of search is independent of node density, the energy consumed is much lower than that of the existing search techniques, and CERS always finds the nearest replica of the target information under high node density.     

A heuristic search algorithm for path determination with learning

We present and analyze an algorithm, adaptive A*(AA*), for finding a least-cost-path from start node to goal node set in a directed graph. Arc costs are assumed to be scalar-valued, and the cost of each path is the sum of the concomitant arc costs. Search is guided by: 1) a collection of real-valued functions on the node set, which is a generalization of the heuristic function associated with A*; 2) a set of predetermined optimal paths; and 3) a set of paths in the graph that are considered desirable but may or may not be optimal. The knowledge representations described in (1) and (3) can be useful in describing knowledge acquired from humans. The knowledge representation described in (2) can be used to automate knowledge acquisition, so that A* exhibits a form of machine learning. Additionally, the collection of real-valued functions on the node set can be useful in describing bounds on the perfect heuristic function, i.e., the solution of the related dynamic program. A numerical analysis, using a specialization of AA* applied to a model of the Cleveland, OH, road network demonstrated significant performance improvement relative to A*     

Heuristic fault-tolerant routing strategies for a multiprocessor network

We examine the suitability of three heuristic search algorithms (Greedy Constructive Scheme, Best First Search and A^*) for use as routing strategies on a faulty multiprocessor network. Our search space is a simulated 5 × 5 × 5 (125-node) multiprocessor mesh network. Each virtual node comprises a processor and a communications switch supporting explicit message backtracking. Their performances are compared for up to 20% of randomly generated faulty links. The results show that heuristic search algorithms can be implemented as fault-tolerant routing strategies and that the modified Best First Search routing strategy performed consistently better with significantly less degradation than the Greedy Constructive Scheme and the A^* strategies.     

Tabu Search within a Pivot and Complement Framework

The Pivot and Complement heuristic is a procedure that frequently finds feasible solutions for general 0–1 integer programs. We present a refinement of the heuristic based on Tabu Search techniques. Local search strategies for the search phase, as well as the improvement phase of the heuristic are presented.     

A new weighted pathfinding algorithms to reduce the search time on grid maps

Artificial Intelligence (AI) techniques are utilized widely in the field of Expert Systems (ES) - as applied to robotics, video games self-driving vehicles and so on. Pathfinding algorithms are a class of heuristic algorithms based on AI techniques which are used in ES as decision making functions for the purpose of solving problems that would otherwise require human competence or expertise. ES fields that use pathfinding algorithms and operate in real-time face many challenges: for example time constraints, optimality and memory overhead for storing the paths which are found. For these algorithms to work, appropriate problem-specific maps must be constructed. In relation to this, the uniform-cost grid set-up is the most appropriate for ES applications. In this method, each node in a graph is represented as a tile, and the weight “between” tiles is set at a constant value, usually this is set to 1. In the state-of-the-art heuristic algorithms used with this data structure, multiplying the heuristic function by a weight greater than one is well-known technique. In this paper, we present three new techniques using various weights to accelerate heuristic search of grid maps. The first such technique is based on the iteration of a heuristic search algorithm associated with weight-set w. The second technique is based on the length between the start node and goal node, which is then associated with w. The last technique is based on the travel cost and is associated with a weight-set α. These techniques are applicable to a wide class of heuristic search algorithms. Therefore, we implement them, here, within the A*, the Bidirectional A* (Bi-A*) and Jump Point Search (JPS) algorithms; thus obtaining a family of new algorithms. Furthermore, it is seen that the use of these new algorithms results in significant improvements over current search algorithms. We evaluate them in path-planning benchmarks and show the amended JPS technique's greater stability, across weight values, over the other two techniques. However, it is also shown that this technique yields poor results in terms of cost solution.     

Empirical analysis of search based algorithms to identify change prone classes of open source software

There are numerous reasons leading to change in software such as changing requirements, changing technology, increasing customer demands, fixing of defects etc. Thus, identifying and analyzing the change-prone classes of the software during software evolution is gaining wide importance in the field of software engineering. This would help software developers to judiciously allocate the resources used for testing and maintenance. Software metrics can be used for constructing various classification models which can be used for timely identification of change prone classes. Search based algorithms which form a subset of machine learning algorithms can be utilized for constructing prediction models to identify change prone classes of software. Search based algorithms use a fitness function to find the best optimal solution among all the possible solutions. In this work, we analyze the effectiveness of hybridized search based algorithms for change prediction. In other words, the aim of this work is to find whether search based algorithms are capable for accurate model construction to predict change prone classes. We have also constructed models using machine learning techniques and compared the performance of these models with the models constructed using Search Based Algorithms. The validation is carried out on two open source Apache projects, Rave and Commons Math. The results prove the effectiveness of hybridized search based algorithms in predicting change prone classes of software. Thus, they can be utilized by the software developers to produce an efficient and better developed software.     

Pharmacy duty scheduling problem

In this study, we define the pharmacy duty scheduling problem, which requires a subset of pharmacies to be on duty on national holidays, at weekends, and at nights, in order to be able to satisfy the emergency medicine needs. We model the pharmacy duty scheduling problem as a multiperiod p‐median problem with special side constraints, and analyze the computational complexity. We propose a Tabu Search heuristic and develop lower bound (LB) algorithms. We test the performance of mathematical models, Tabu Search heuristic, and the LBs on randomly generated instances. We analyze the current system in ?zmir, the third largest city in Turkey, with a population of 3.5 million, and apply solution methods. Our results show that the proposed Tabu Search algorithm suggests improvements on the current system.     

Sequential search and its application to vehicle-routing problems

Local search is the most frequently used heuristic technique for solving combinatorial optimization problems. It is also the basis for modern metaheuristics, like, e.g., Tabu Search, and Variable Neighborhood Search. The paper introduces sequential search as a generic technique for the efficient exploration of local-search neighborhoods. One of its key concepts is the systematic decomposition of moves, which allows pruning within local search based on associated partial gains. The application of theoretical concepts to several well-known neighborhoods of the vehicle-routing problem (VRP) is demonstrated. Computational tests show substantial speedup factors, e.g., up to 10 000 for the 3-opt* neighborhood. This underlines the superiority of sequential search over straightforward techniques in the VRP context.     

一种基于道路网络层次拓扑结构的分层路径规划算法

鉴于平面最短路径算法应用于大规模网络规划中的效率不高,而分层算法引入"分而治之"策略,则能有效解决此难题。为了利用分层算法进行路径规划,首先研究了分层算法的数据基础——道路网络层次拓扑结构,其涉及基于道路等级的路网分层抽象、道路数据分区组织、以区域为单位的路网层次拓扑关系模型;接着提出了一种适用于LBS(基于位置的服务)的分层路径规划算法。该算法先通过距离值判断是否切换到上一层;然后利用启发式A*算法搜索入口和出口;最后使用双向策略搜索层内两点之间的最短路径。利用现实道路网络进行的实验分析结果表明,该算法能从本质上提高大规模网络中路径规划的效率。     

Combinations of estimation of distribution algorithms and other techniques

This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems:a) guided mutation,an offspring generator in which the ideas from EDAs and genetic algorithms are combined together,we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique problem,b)evolutionary algorithms refining a heuristic,we advocate a strategy for solving a hard optimization problem with complicated data structure,and c) combination of two different local search techniques and EDA for numerical global optimization problems,its basic idea is that not all the new generated points are needed to be improved by an expensive local search.     

ATM实时网络拥塞控制的有效CAC法则

文中介绍拥塞控制下ATM实时网络的一种自适应实时连接模型。提出如何在新模型下接受实时连接,并研究了不同性能标准下的启发式搜索法则。采用这种方法可以在提供最好可能的QoS下增加网络拥塞下连接被接受的可能。     

Embedded local search approaches for routing optimization

This paper presents a new class of heuristics which embed an exact algorithm within the framework of a local search heuristic. This approach was inspired by related heuristics which we developed for a practical problem arising in electronics manufacture. The basic idea of this heuristic is to break the original problem into small subproblems having similar properties to the original problem. These subproblems are then solved using time intensive heuristic approaches or exact algorithms and the solution is re-embedded into the original problem. The electronics manufacturing problem where we originally used the embedded local search approach, contains the Travelling Salesman Problem (TSP) as a major subproblem. In this paper we further develop our embedded search heuristic, HyperOpt, and investigate its performance for the TSP in comparison to other local search based approaches. We introduce an interesting hybrid of HyperOpt and 3-opt for asymmetric TSPs which proves more efficient than HyperOpt or 3-opt alone. Since pure local search seldom yields solutions of high quality we also investigate the performance of the approaches in an iterated local search framework. We examine iterated approaches of Large-Step Markov Chain and Variable Neighbourhood Search type and investigate their performance when used in combination with HyperOpt. We report extensive computational results to investigate the performance of our heuristic approaches for asymmetric and Euclidean Travelling Salesman Problems. While for the symmetric TSP our approaches yield solutions of comparable quality to 2-opt heuristic, the hybrid methods proposed for asymmetric problems seem capable of compensating for the time intensive embedded heuristic by finding tours of better average quality than iterated 3-opt in many less iterations and providing the best heuristic solutions known, for some instance classes.     

Variable Neighborhood Search heuristic for the Inventory Routing Problem in fuel delivery

In this paper we observe the extension of the vehicle routing problem (VRP) in fuel delivery that includes petrol stations inventory management and which can be classified as the Inventory Routing Problem (IRP) in fuel delivery. The objective of the IRP is to minimize the total cost of vehicle routing and inventory management. We developed a Variable Neighborhood Search (VNS) heuristic for solving a multi-product multi-period IRP in fuel delivery with multi-compartment homogeneous vehicles, and deterministic consumption that varies with each petrol station and each fuel type. The stochastic VNS heuristic is compared to a Mixed Integer Linear Programming (MILP) model and the deterministic “compartment transfer” (CT) heuristic. For three different scale problems, with different vehicle types, the developed VNS heuristic outperforms the deterministic CT heuristic. Also, for the smallest scale problem instances, the developed VNS was capable of obtaining the near optimal and optimal solutions (the MILP model was able to solve only the smallest scale problem instances).     

Applying a fuzzy sets‐based heuristic to the protein structure prediction problem

The interface between combinatorial optimization and fuzzy sets‐based methodologies is the subject of very active and increasing research. In this context we describe FANS, a fuzzy adaptive neighborhood search optimization heuristic that uses a fuzzy valuation to qualify solutions and adapts its behavior as a function of the search state. FANS may also be regarded as a local search framework. We show the application of this fuzzy sets‐based heuristic to the protein structure prediction problem in two aspects: first, to analyze how the codification of the solutions affects the results, and second, to confirm that FANS is able to obtain as good results as a genetic algorithm. Both results shed some light on the application of heuristics to the protein structure prediction problem and show the benefits and power of combining basic fuzzy sets ideas with heuristic techniques. © 2002 Wiley Periodicals, Inc.     

Discrete symbiotic organisms search algorithm for travelling salesman problem

A Discrete Symbiotic Organisms Search (DSOS) algorithm for finding a near optimal solution for the Travelling Salesman Problem (TSP) is proposed. The SOS is a metaheuristic search optimization algorithm, inspired by the symbiotic interaction strategies often adopted by organisms in the ecosystem for survival and propagation. This new optimization algorithm has been proven to be very effective and robust in solving numerical optimization and engineering design problems. In this paper, the SOS is improved and extended by using three mutation-based local search operators to reconstruct its population, improve its exploration and exploitation capability, and accelerate the convergence speed. To prove that the proposed solution approach of the DSOS is a promising technique for solving combinatorial problems like the TSPs, a set of benchmarks of symmetric TSP instances selected from the TSPLIB library are used to evaluate its performance against other heuristic algorithms. Numerical results obtained show that the proposed optimization method can achieve results close to the theoretical best known solutions within a reasonable time frame.     

Web search enhancement by mining user actions

Search engines are among the most popular as well as useful services on the web. There is a need, however, to cater to the preferences of the users when supplying the search results to them. We propose to maintain the search profile of each user, on the basis of which the search results would be determined. This requires the integration of techniques for measuring search quality, learning from the user feedback and biased rank aggregation, etc. For the purpose of measuring web search quality, the “user satisfaction” is gauged by the sequence in which he picks up the results, the time he spends at those documents and whether or not he prints, saves, bookmarks, e-mails to someone or copies-and-pastes a portion of that document. For rank aggregation, we adopt and evaluate the classical fuzzy rank ordering techniques for web applications, and also propose a few novel techniques that outshine the existing techniques. A “user satisfaction” guided web search procedure is also put forward. Learning from the user feedback proceeds in such a way that there is an improvement in the ranking of the documents that are consistently preferred by the users. As an integration of our work, we propose a personalized web search system.     

Integrated approach of network reconfiguration with distributed generation and shunt capacitors placement for power loss minimization in radial distribution networks

This article presents the significance of efficient hybrid heuristic search algorithm(HS-PABC) based on Harmony search algorithm (HSA) and particle artificial bee colony algorithm (PABC) in the context of performance enhancement of distribution network through simultaneous network reconfiguration along with optimal allocation and sizing of distributed generators and shunt capacitors. The premature and slow convergence over multi model fitness landscape is the main limitation in standard HSA. In the proposed hybrid algorithm the harmony memory vector of HSA is intelligently enhanced through PABC algorithm during the optimization process to reach the optimal solution within the search space. In hybrid approach, the exploration ability of HSA and the exploitation ability of PABC algorithm are integrated to blend the potency of both algorithms. The box plot and Wilcoxon rank sum tests are used to show the quality of the solution obtained by hybrid HS-PABC with respect to HSA.The computational results prove the integrated approach of the network reconfiguration problem along with optimal placement and sizing of DG units and shunt capacitors as an efficient approach with respect to power loss reduction and voltage profile enhancement. The results obtained on 69 and 118 node network by hybrid HS-PABC method and the standard HSA reveals the effeciency of the proposed approach which guarantees to achieve global optimal solution with less iteration.     

Enhancing Search Performance on Gnutella-Like P2P Systems

The big challenges facing the search techniques on Gnutella-like peer-to-peer networks are search efficiency and quality of search results. In this paper, leveraging information retrieval (IR) algorithms such as Vector Space Model (VSM) and relevance ranking algorithms, we present GES (Gnutella with Efficient Search) to improve search performance. The key idea is that GES uses a distributed topology adaptation algorithm to organize semantically relevant nodes into same semantic groups by using the notion of node vector. Given a query, GES employs an efficient search protocol to direct the query to the most relevant semantic groups for answers, thereby achieving high recall with probing only a small fraction of nodes. To the best of our knowledge, GES is the first to identify node vector size as an important role in impacting search performance and to show that the node vector size offers a good trade-off between search performance and bandwidth cost. Moreover, GES adopts automatic query expansion and local data clustering to improve search performance. We show that GES is efficient and even outperforms the centralized node clustering system SETS. For example, in the scenario where node capacity is heterogeneous, GES can achieve 73 percent recall when probing only 20 percent nodes, outperforming SETS by about 18 percent.