Search For Multiple Solutions Research Articles

QiBAM: Approximate Sub-String Index Search on Quantum Accelerators Applied to DNA Read Alignment

With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors in a few years are expected to scale up and be more robust for efficiently computing important algorithms in various fields. In this paper, we propose a quantum algorithm to address the challenging field of data processing for genome sequence reconstruction. This research describes an architecture-aware implementation of a quantum algorithm for sub-sequence alignment. A new algorithm named QiBAM (quantum indexed bidirectional associative memory) is proposed, which uses approximate pattern-matching based on Hamming distances. QiBAM extends the Grover’s search algorithm in two ways, allowing: (1) approximate matches needed for read errors in genomics, and (2) a distributed search for multiple solutions over the quantum encoding of DNA sequences. This approach gives a quadratic speedup over the classical algorithm. A full implementation of the algorithm is provided and verified using the OpenQL compiler and QX Simulator framework. Our implementation represents a first exploration towards a full-stack quantum accelerated genome sequencing pipeline design.

GPU parallel neural hierarchical multi objective solver for burst routing and wavelength assignment

Optical Burst Switching (OBS) is a promising technology for next generation of Transparent Optical Networks (TON). However, many scientific challenges remain to be overcome such as the problem of Burst Routing and Wavelength Assignment (BRWA) with several conflicting objectives and constraints. In this paper, we first formulate the BRWA as a Multi Objective Integer Linear Programming (MO-ILP) optimization problem.In the formulated problem, the proposed BRWA policy will satisfy several constraints in order to guarantee a high-speed management of processes, required by the transparent optical traffic. Then, since the obtained ILP problem contains a large number of optical constraints and conflicting objectives, we propose to use an exact parallel Neural Hierarchical (epNH) MO-ILP solution with Graphics Processing Unit (GPU) parallel implementation using Compute Unified Device Architecture (CUDA). This also allows doing a concurrent search for multiple solutions, reducing processing cost, making hybrid interfaces to other search techniques, and achieving better overall effectiveness.In addition, our architecture based on Artificial Neural Networks (ANN) allows flexibility and scalability. The processing time remains fixed regardless of the input size. Our BRWA GPU-based epNH MO-ILP solver is based on the joint use of advanced MO-ILP optimization methods, ANN large-scale inherent parallelism and CUDA-GPU High-Performance Computing (HPC) architecture.

An improved artificial bee colony algorithm based on the gravity model

Artificial bee colony (ABC) algorithm is a relatively new biologically-inspired optimization algorithm. According to its solution search equation, it can be seen that ABC is good at exploration but poor at exploitation. Inspired by the gravity model, an attractive force model is proposed for choosing a better neighbor of a current individual to improve the exploitation ability of ABC. Then we propose a novel solution search equation, in which the chosen neighbor plays an important role in guiding the searching process in the employed bee phase. Next, a random guiding search is introduced in the onlooker bee phase to balance the foregoing exploitation. Subsequently, multiple solution search equations, a scheme of perturbation frequency, and a multiple scouts search strategy in view of opposition-based learning are also incorporated into the proposed algorithm, called ABCG, to further reach a good compromise between the exploitation and the exploration. Finally, ABCG is tested on a great number of benchmark functions. The experimental results show that ABCG is effective for solving the complex benchmark problems and it can be considered as a competitive approach.

Application of α-stable mutation in a hierarchic evolutionary inverse solver

The multi-deme Hierarchic Genetic Strategy (HGS) developed at the end of the 20th century already proved its capabilities of solving ill-conditioned multi-modal continuous global optimization problems, both benchmarks and real-world engineering inversions. As a standard it uses the mutation operator based on the normal probability distribution. It is a common choice in the continuous evolutionary optimization, but in practice it exhibits some properties that significantly reduce its exploratory abilities, which are crucial in the search for multiple solutions. Those drawbacks can be largely overcome if we replace the normal distribution with a special α-stable distribution for α<2. In this paper, we study the application of such distribution in the HGS mutation operator. First, we execute standard multi-modal benchmarks to show the impact of particular values of the stable distribution parameters. Then, using selected values of those parameters we employ the HGS with the α-stable mutation in solving an advanced ill-conditioned inverse parametric problem connected to the oil and gas resource investigation. The obtained results show that the α-stable mutation delivers more solutions than the classical normal mutation within a slightly better time budget. Another important conclusion is that the number of solutions is significantly more predictable in the case of α-stable mutation, which is a very advantageous feature from the point of view of the application of a stochastic strategy in the inverse problem solution.

Stimulating Creativity: Modulation of Convergent and Divergent Thinking by Transcranial Direct Current Stimulation (tDCS)

Creativity has been conceptualized as involving 2 distinct components; divergent thinking, the search for multiple solutions to a single problem, and convergent thinking, the quest for a single solution either through an analytical process or the experience of insight. Studies have demonstrated that these abilities can be improved by cognitive stimulation, mood, and meditation. This investigation examined whether convergent and divergent thinking can be enhanced by noninvasive transcranial direct current stimulation (tDCS). In different sessions, participants received bilateral stimulation over the dorsolateral prefrontal cortex—DLPFC (Experiment1) and over the posterior parietal cortex–PPC (Experiment2), while performing the Compound Remote Associative task (CRA) assessing convergent thinking and the Alternative Uses Task (AUT) assessing divergent thinking. In Experiment1, anodal-left cathodal-right stimulation over the DLPFC significantly enhanced CRA performance. In Experiment2, stimulations over the PPC significantly increased insight solutions and decreased analytical solutions compared to the no stimulation condition. These findings provide direct evidence for the role of the left DLPFC in convergent and divergent thinking and a mediating role of the PPC in problem-solving behavior, presumably through attentional processes. From a methodological perspective, brain stimulation can be used as a tool to modulate and to explore components of creativity.

Computing multiple periodic solutions of nonlinear vibration problems using the harmonic balance method and Groebner bases

This paper is devoted to the study of vibration of mechanical systems with geometric nonlinearities. The harmonic balance method is used to derive systems of polynomial equations whose solutions give the frequency component of the possible steady states. Groebner basis methods are used for computing all solutions of polynomial systems. This approach allows to reduce the complete system to an unique polynomial equation in one variable driving all solutions of the problem. In addition, in order to decrease the number of variables, we propose to first work on the undamped system, and recover solution of the damped system using a continuation on the damping parameter. The search for multiple solutions is illustrated on a simple system, where the influence of the retained number of harmonic is studied. Finally, the procedure is applied on a simple cyclic system and we give a representation of the multiple states versus frequency.

Study on Improvement of Memory Cell Control in Hybridization of Immune Algorithm and Gradient Search for Multiple Solution Search

Many evolutionary computation methods have been proposed and applied to real world problems. But gradient methods are still effective in problems involving real-coded parameters. In addition, it is desirable to find not only an optimal solution but also plural optimal and semi-optimal solutions in most real world problems. Although a hybrid algorithm combining Immune Algorithm (IA) and Quasi-Newton method (QN) has been proposed for multiple solution search, its memory cell control sometimes fails to keep semi-optimal solutions whose evaluation value is not so high. In addition, because the hybrid algorithm applies QN only to memory cell candidates, QN can be used as local search operator only after global search by IA. This paper proposes an improved memory cell control which restricts existence of redundant memory cells, and a QN application method which uses QN even in early search stage. Experimental results have shown that the hybrid algorithm involving the proposed improvements can find optimal and semi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal functions involving epistasis.