30151415
Genetic Algorithm Based-On the Quantum Probability Representation
A genetic algorithm based on the quantum probability representation (GAQPR) is proposed, in which each individual evolves independently; a new crossover operator is designed to integrate searching processes of multiple individuals into a more efficient global searching process; a new mutation operator is also proposed and analyzed. Optimization capability of GAQPR is studied via experiments on function optimization, results of experiments show that, for multi-peak optimization problem, GAQPR is more efficient than GQA[4].
17592710
Full-Space LDA With Evolutionary Selection for Face Recognition
Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix (Sw). However, experimental results testified that not all the eigenvectors in the full space of S w are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of Sw by selecting an optimal subset of eigenvectors. An estimation of distribution algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of Sw middot EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods
29843270
A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
Large scale global optimization (LSGO), which is highly needed for many scientific and engineering applications, is a very important and very difficult task in optimization domain. Various algorithms have been proposed to tackle this challenging problem, but the use of estimation of distribution algorithms (EDAs) to it is rare. This chapter aims at investigating the behavior and performances of uni-variate EDAs mixed with different kernel probability densities via fitness landscape analysis. Based on the analysis, a self-adaptive uni-variate EDA with mixed kernels (MUEDA) is proposed. To assess the effectiveness and efficiency of MUEDA, function optimization tasks with dimension scaling from 30 to 1500 are adopted. Compared to the recently published LSGO algorithms, MUEDA shows excellent convergence speed, final solution quality and dimensional scalability.
3197098
A Multi-Objective Hw-sw Co-Synthesis Algorithm Based on Quantum-Inspired Evolutionary Algorithm
Hardware–Software (HW–SW) co-synthesis is one of the key steps in modern embedded system design. Generally, HW–SW co-synthesis is to optimally allocate processors, assign tasks to processors, and schedule the processing of tasks to achieve a good balance among performance, cost, power consumption, etc. Hence, it is a typical multi-objective optimization problem. In this paper, a new multi-objective HW–SW co-synthesis algorithm based on the quantum-inspired evolutionary algorithm (MQEAC) is proposed. MQEAC utilizes multiple quantum probability amplitude vectors to model the promising areas of solution space. Meanwhile, this paper presents a new crossover operator to accelerate the convergence to the Pareto front and introduces a PE slot-filling strategy to improve the efficiency of scheduling. Experimental results show that the proposed algorithm can solve the typical multi-objective co-synthesis problems effectively and efficiently.
9214943
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution
A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.
26519336
Iris Recognition Algorithm Using Modified Log-Gabor Filters
In this paper, we presented an iris recognition algorithm based on modified Log-Gabor filters. The algorithm is similar as the method proposed by Daugman in general procedure while modified Log-Gabor filters are adopted to extract the iris phase information instead of complex Gabor filters used in Daugman's method. The advantage of Log-Gabor filters over complex Gabor filters is the former are strictly bandpass filters and the latter are not. The property of strictly bandpass makes the Log-Gabor filters more suitable to extract the iris phase features regardless of the background brightness. The comparison experiments between complex Gabor filters based methods and the proposed method are also presented in this paper
25955132
A new synchronization algorithm for VHDL-AMS simulation
VHDL-AMS is the Analog and Mixed-Signal Extensions to VHDL. The paper gives a brief overview of the added features to VHDL. A mixed-signal simulator has been developed based on VHDL-AMS. A new synchronization algorithm is adopted in the simulator. Using the new algorithm the analog kernel does not need to synchronize the digital kernel at each digital event time point. The efficiency of the new synchronization algorithm is tested by examples. Simulation results show the newly developed algorithm can speed up the simulation.
37867009
Spatial Clustering with Obstacles Constraints Using Ant Colony and Particle Swarm Optimization
Spatial clustering is an important research topic in Spatial Data Mining (SDM). This paper proposes an Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) method for solving Spatial Clustering with Obstacles Constraints (SCOC). In the process of doing so, we first use improved ACO to obtain the shortest obstructed distance, which is an effective method for arbitrary shape obstacles, and then we develop a novel PKSCOC based on PSO and K-Medoids to cluster spatial data with obstacles. The PKSCOC algorithm can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering. The experimental results demonstrate the effectiveness and efficiency of the proposed method, which performs better than Improved K-Medoids SCOC (IKSCOC) in terms of quantization error and has higher constringency speed than Genetic K-Medoids SCOC (GKSCOC).
25793288
Polaris: A System-Level Roadmapping Toolchain for On-Chip Interconnection Networks
Technology trends are driving parallel on-chip architectures in the form of multiprocessor systems-on-a-chip (MPSoCs) and chip multiprocessors (CMPs). In these systems, the increasing on-chip communication demand among the computation elements necessitates the use of scalable, high-bandwidth network-on-chip (NoC) fabrics instead of dedicated interconnects and shared buses. As transistor feature sizes are further miniaturized, more complicated NoC architectures become feasible that can support more demanding applications. Given the myriad emerging software-hardware combinations, for cost-effectiveness, a system designer critically needs to prune this widening NoC design-space to predict the interconnect fabric(s) that best balance(s) cost/performance, before the actual design process begins. This prompted us to develop Polaris, a system-level roadmapping toolchain for on-chip interconnection networks that helps designers predict the most suitable interconnection network design(s) tailored to their performance needs and power/silicon area constraints with respect to a range of applications that the system will run. Polaris explores the plethora of NoC designs based on projections of network traffic, architectures, and process characteristics. While Polaris's toolchain is extensible so new traffic, network designs, and technology processes can be added, the current version already incorporates 7872 NoC design points. Polaris is rapid, efficiently iterating over thousands of NoC design points, while maintaining high relative and absolute accuracies when validated against detailed NoC synthesis results.
8359291
Polaris: A System-Level Roadmap for On-Chip Interconnection Networks
Technology trends are driving parallel on-chip architectures in the form of multi-processor systems-on-a-chip (MPSoCs) and chip multi-processors (CMPs). In these systems the increasing on-chip communication demand among the computation elements necessitates the use of scalable, nigh-bandwidth network-on-chip (NoC) fabrics. As transistor feature sizes are further miniaturized leading to rapidly increasing amounts of on-chip resources, more complicated and powerful NoC architectures become feasible that can support more sophisticated and demanding applications. Given the myriad emerging software-hardware combinations, for cost-effectiveness, a system designer critically needs to prune this widening NoC design space to identify the architecture(s) that best balance(s) cost/performance, before the actual design process begins. This prompted us to develop Polaris 1, a system-level roadmap for on-chip interconnection networks that guides designers towards the most suitable network design(s) tailored to their performance needs and power/silicon area constraints with respect to a range of applications that will run over this network(s). Polaris explores the plethora of NoC designs based on projections of network traffic, architectures, and process characteristics. While the Polaris roadmapping toolchain is extensible so new traffic, network designs, and processes can be added, the current version of the roadmap already incorporates 7,872 NoC design points. Polaris is rapid and iterates over all these NoC architectures within a tractable run time of 125 hours on a typical desktop machine, while maintaining high relative and absolute accuracies when validated against detailed NoC synthesis results.
14401068
Nitrogen and Phosphate Removal by Zeolite-Rare Earth Adsorbents
Zeolite modified by rare earth lanthanum compounds would be an excellent water purification agent that can simultaneously remove both nitrogen and phosphate in aqueous solution. In the context, a novel adsorbent was prepared by loading lanthanum chloride on the zeolite, and the the optimal sorbent was produced at 0.5% La3+ and pH value 10 in the solution. Remove ammonia nitrogen and phosphate synchronously tests result showed that the new sorbent is very effective for ammonia nitrogen and phosphate removal, and the best adsorption capacity is 25mg/g and 50mg/g respectively. Feeding into 10 mg/L NH4+-N and 5mg/L H2PO4- in wastewater, the removal ratio will be over 80% and 90% respectively, and dynamics for the process is accordance to Freundlich adsorption model. The result suggests that the novel rare earth adsorbent will provide a new way for the treatment of ammonia nitrogen and phosphate in wastewater.