26523507
MeSwarm: memetic particle swarm optimization
In this paper, a novel variant of particle swarm optimization (PSO), named memetic particle swarm optimization algorithm (MeSwarm), is proposed for tackling the overshooting problem in the motion behavior of PSO. The overshooting problem is a phenomenon in PSO due to the velocity update mechanism of PSO. While the overshooting problem occurs, particles may be led to wrong or opposite directions against the direction to the global optimum. As a result, MeSwarm integrates the standard PSO with the Solis and Wets local search strategy to avoid the overshooting problem and that is based on the recent probability of success to efficiently generate a new candidate solution around the current particle. Thus, six test functions and a real-world optimization problem, the flexible protein-ligand docking problem are used to validate the performance of MeSwarm. The experimental results indicate that MeSwarm outperforms the standard PSO and several evolutionary algorithms in terms of solution quality.
5744211
Intelligent evolutionary algorithms for large parameter optimization problems
This work proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents into N pairs of gene segments, economically identifying the potentially better one of two gene segments of each pair, and systematically obtaining a potentially good approximation to the best one of all combinations using at most 2N fitness evaluations. IMOEA utilizes a novel generalized Pareto-based scale-independent fitness function for efficiently finding a set of Pareto-optimal solutions to a multiobjective optimization problem. The advantages of IEA and IMOEA are their simplicity, efficiency, and flexibility. It is shown empirically that IEA and IMOEA have high performance in solving benchmark functions comprising many parameters, as compared with some existing EAs.
12562104
Collaborative information agents on the World Wide Web
In this paper, we present DIAMS, a system of distributed, collaborative information agents which help users access, collect, organize and exchange information on the World Wide Web. Personal agents provide their owners dynamic displays of well organized information collections, as well as friendly information management utilities. Personal agents exchange information with one another. They also work with other types of information agents such as matchmakers and knowledge experts to facilitate collaboration and communication.
2027380
A More Secure and Efficacious TTS Signature Scheme
In 2002 the authors introduced the new genre of digital signature scheme TTS (Tame Transformation Signatures) along with a sample scheme TTS/2. TTS is from the family of multivariate cryptographic schemes to which the NESSIE primitive SFLASH also belongs. It is a realization of T. Moh's theory ([31]) for digital signatures, based on Tame Transformations or Tame Maps. Properties of multivariate cryptosystems are determined mainly by their central maps. TTS uses Tame Maps as their central portion for even greater speed than C*-derived SFLASH family of schemes, which uses monomials in a large field for the central portion, previously usually acknowledged as fastest. We show a small flaw in TTS/2 and present an improved TTS implementation which we call TTS/4. We will examine in some detail how well TTS/4 performs, how it stands up to previously known attacks, and why it represents an advance over TTS/2. Based on this topical assessment, we consider TTS in general and TTS/4 in particular to be competitive or superior in several aspects to other schemes, partly because the theoretical roots of TTS induce many good traits. One specific area in which TTS/4 should excel is in low-cost smartcards. It seems that the genre has great potential for practical deployment and deserves further attention by the cryptological community.
37268320
On the Approximability of Multiprocessor Task Scheduling Problems
The multiprocessor job scheduling problem has received considerable attention recently. An extensive list of approximation algorithms has been developed and studied for the problem under a variety of constraints. In this paper, we show that from the viewpoint of approximability, the general multiprocessor job scheduling problem has a very rich structures such that by putting simple constraints on the number of processors in the system, we can obtain four versions of the problem, which are NP-hard with a fully polynomial time approximation scheme, strongly NP-hard with a polynomial time approximation scheme, APX-complete (thus with a constant approximation ratio in polynomial time), and with no constant approximation ratio in polynomial time, respectively.
1746401
On the Effective Enumerability of NP Problems
In the field of computational optimization, it is often the case that we are given an instance of an NP problem and asked to enumerate the first few best solutions to the instance. Motivated by this, we propose in this paper a new framework to measure the effective enumerability of NP optimization problems. More specifically, given an instance of an NP problem, we consider the parameterized problem of enumerating a given number of best solutions to the instance, and study its average complexity in terms of the number of solutions. Our framework is different from the previously-proposed ones. For example, although it is known that counting the number of k-paths in a graph is #W[1]-complete, we present a fixed-parameter enumeration algorithm for the problem. We show that most algorithmic techniques for fixed-parameter tractable problems, such as search trees, color coding, and bounded treewidth, can be used for parameterized enumerations. In addition, we design elegant and new enumeration techniques and show how to generate small-size structures and enumerate solutions efficiently.
18252662
On Constrained Minimum Vertex Covers of Bipartite Graphs: Improved Algorithms
The constrained minimum vertex cover problem on bipartite graphs arises from the extensively studied fault coverage problem for reconfigurable arrays. In this paper, we develop a new algorithm for the problem, in which classical results in matching theory and recently developed techniques in parameterized computation are nicely combined and extended. The algorithm is practically efficient with running time bounded by O(1.26k + kn), where k is the size of the constrained minimum vertex cover in the input graph. The algorithm is a significant improvement over the previous algorithms for the problem.