scholarly journals ODSS: Efficient Hybridization for Optimal Coalition Structure Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 7079-7086
Author(s):  
Narayan Changder ◽  
Samir Aknine ◽  
Sarvapali Ramchurn ◽  
Animesh Dutta
Keyword(s):  
Hybrid Algorithm ◽  
State Of The Art ◽  
Coalition Structure ◽  
Search Space ◽  
The State ◽  
Structure Generation ◽  
Complete Problem ◽  
Disjoint Sets ◽  
Np Complete ◽  
Coalition Structure Generation

Coalition Structure Generation (CSG) is an NP-complete problem that remains difficult to solve on account of its complexity. In this paper, we propose an efficient hybrid algorithm for optimal coalition structure generation called ODSS. ODSS is a hybrid version of two previously established algorithms IDP (Rahwan and Jennings 2008) and IP (Rahwan et al. 2009). ODSS minimizes the overlapping between IDP and IP by dividing the whole search space of CSG into two disjoint sets of subspaces and proposes a novel subspace shrinking technique to reduce the size of the subspace searched by IP with the help of IDP. When compared to the state-of-the-art against a wide variety of value distributions, ODSS is shown to perform better by up to 54.15% on benchmark inputs.

scholarly journals Coalition Structure Generation over Graphs

2012 ◽  
Vol 45 ◽  
pp. 165-196 ◽  
Author(s):  
T. Voice ◽  
M. Polukarov ◽  
N. R. Jennings
Keyword(s):  
Linear Time ◽  
Coalition Structure ◽  
Valuation Function ◽  
Structure Generation ◽  
Induced Subgraph ◽  
Bounded Treewidth ◽  
Vertex Separator ◽  
Np Complete ◽  
Minor Free Graphs ◽  
Coalition Structure Generation

We give the analysis of the computational complexity of coalition structure generation over graphs. Given an undirected graph G = (N,E) and a valuation function v : P(N) → R over the subsets of nodes, the problem is to find a partition of N into connected subsets, that maximises the sum of the components’ values. This problem is generally NP–complete; in particular, it is hard for a defined class of valuation functions which are independent of disconnected members—that is, two nodes have no effect on each other’s marginal con- tribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minor–free graphs. Our proof is constructive and yields algorithms for solving corresponding instances of the problem. Furthermore, we derive linear time bounds for graphs of bounded treewidth. However, as we show, the problem remains NP–complete for planar graphs, and hence, for any K_k minor–free graphs where k ≥ 5. Moreover, a 3-SAT problem with m clauses can be represented by a coalition structure generation problem over a planar graph with O(m^2) nodes. Importantly, our hardness result holds for a particular subclass of valuation functions, termed edge sum, where the value of each subset of nodes is simply determined by the sum of given weights of the edges in the induced subgraph.

Robust Coalition Structure Generation

2018 ◽  
pp. 140-157
Author(s):  
Tenda Okimoto ◽  
Nicolas Schwind ◽  
Emir Demirović ◽  
Katsumi Inoue ◽  
Pierre Marquis
Keyword(s):  
Coalition Structure ◽  
Structure Generation ◽  
Coalition Structure Generation

scholarly journals Particle Swarm Contour Search Algorithm

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 407 ◽  
Author(s):  
Dominik Weikert ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Image Processing ◽  
Real World ◽  
State Of The Art ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Local Information ◽  
The State ◽  
Complete Knowledge ◽  
Real World Applications

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.

GRAPH BISECTION MODELED AS CARDINALITY CONSTRAINED BINARY QUADRATIC TASK ALLOCATION

2013 ◽  
Vol 12 (02) ◽  
pp. 261-276 ◽  
Author(s):  
MARK LEWIS ◽  
GARY KOCHENBERGER
Keyword(s):  
Task Allocation ◽  
Graph Partitioning ◽  
State Of The Art ◽  
Quadratic Model ◽  
Complete Problem ◽  
Binary Model ◽  
Comparison Results ◽  
Graph Bisection ◽  
Strategic Oscillation ◽  
Np Complete

In this paper, the cardinality constrained quadratic model for binary quadratic programming is used to model and solve the graph bisection problem as well as its generalization in the form of the task allocation problem with two processors (2-TAP). Balanced graph bisection is an NP-complete problem which partitions a set of nodes in the graph G = (N, E) into two sets with equal cardinality such that a minimal sum of edge weights exists between the nodes in the two separate sets. 2-TAP is graph bisection with the addition of node preference costs in the objective function. We transform the general linear k-TAP model to the cardinality constrained quadratic binary model so that it may be efficiently solved using tabu search with strategic oscillation. On a set of benchmark graph bisections, we improve the best known solution for several problems. Comparison results with the state-of-the-art graph partitioning program METIS, as well as Cplex and Gurobi are presented on a set of randomly generated graphs. This approach is shown to also work well with 2-TAP, comparing favorably to Cplex and Gurobi, providing better solutions in a much shorter time.

EFFICIENT APPROACH FOR WEB SERVICES SELECTION WITH MULTI-QoS CONSTRAINTS

2008 ◽  
Vol 17 (03) ◽  
pp. 349-371 ◽  
Author(s):  
TAO HUANG ◽  
LEI LI ◽  
JUN WEI
Keyword(s):  
Web Services ◽  
Polynomial Time ◽  
High Performance ◽  
Feasible Solution ◽  
Search Space ◽  
User Preference ◽  
Composite Service ◽  
Efficient Approach ◽  
Complete Problem ◽  
Np Complete

With the increasing number of Web Services with similar or identical functionality, the non-functional properties of a Web Service will become more and more important. Hence, a choice needs to be made to determine which services are to participate in a given composite service. In general, multi-QoS constrained Web Services composition, with or without optimization, is a NP-complete problem on computational complexity that cannot be exactly solved in polynomial time. A lot of heuristics and approximation algorithms with polynomial- and pseudo-polynomial-time complexities have been designed to deal with this problem. However, these approaches suffer from excessive computational complexities that cannot be used for service composition in runtime. In this paper, we propose a efficient approach for multi-QoS constrained Web Services selection. Firstly, a user preference model was proposed to collect the user's preference. And then, a correlation model of candidate services are established in order to reduce the search space. Based on these two model, a heuristic algorithm is then proposed to find a feasible solution for multi-QoS constrained Web Services selection with high performance and high precision. The experimental results show that the proposed approach can achieve the expecting goal.

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