scholarly journals A molecular computing approach to solving optimization problems via programmable microdroplet arrays

Matter ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 1107-1124
Author(s):  
Si Yue Guo ◽  
Pascal Friederich ◽  
Yudong Cao ◽  
Tony C. Wu ◽  
Christopher J. Forman ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Molecular Computing ◽  
Computing Approach

scholarly journals Shortest Path Computing in Directed Graphs with Weighted Edges Mapped on Random Networks of Memristors

2020 ◽  
Vol 30 (01) ◽  
pp. 2050002
Author(s):  
Carlos Fernandez ◽  
Ioannis Vourkas ◽  
Antonio Rubio
Keyword(s):  
Shortest Path ◽  
Optimization Problems ◽  
Circuit Simulation ◽  
Directed Graphs ◽  
Switching Behavior ◽  
Large Set ◽  
Open Problems ◽  
Time Required ◽  
The Impact ◽  
Computing Approach

To accelerate the execution of advanced computing tasks, in-memory computing with resistive memory provides a promising solution. In this context, networks of memristors could be used as parallel computing medium for the solution of complex optimization problems. Lately, the solution of the shortest-path problem (SPP) in a two-dimensional memristive grid has been given wide consideration. Some still open problems in such computing approach concern the time required for the grid to reach to a steady state, and the time required to read the result, stored in the state of a subset of memristors that represent the solution. This paper presents a circuit simulation-based performance assessment of memristor networks as SPP solvers. A previous methodology was extended to support weighted directed graphs. We tried memristor device models with fundamentally different switching behavior to check their suitability for such applications and the impact on the timely detection of the solution. Furthermore, the requirement of binary vs. analog operation of memristors was evaluated. Finally, the memristor network-based computing approach was compared to known algorithmic solutions to the SPP over a large set of random graphs of different sizes and topologies. Our results contribute to the proper development of bio-inspired memristor network-based SPP solvers.

scholarly journals A Molecular Computing Approach to Solving Optimization Problems via Programmable Microdroplet Arrays

2019 ◽  
Author(s):  
Si Yue Guo ◽  
Pascal Friederich ◽  
Yudong Cao ◽  
Tony Wu ◽  
Christopher Forman ◽  
...  
Keyword(s):  
Chemical Reactions ◽  
Optimization Problems ◽  
Research Direction ◽  
Molecular Computing ◽  
Effective Spin ◽  
Combinatorial Optimization Problems ◽  
Protein Model ◽  
Computer Based ◽  
Classical Computer ◽  
Molecular Computers

The search for novel forms of computing that show advantages as alternatives to the dominant von-Neuman model-based computing is important as it will enable different classes of problems to be solved. By using droplets and room-temperature processes, molecular computing is a promising research direction with potential biocompatibility and cost advantages. In this work, we present a new approach for computation using a network of chemical reactions taking place within an array of spatially localized droplets whose contents represent bits of information. Combinatorial optimization problems are mapped to an Ising Hamiltonian and encoded in the form of intra- and inter- droplet interactions. The problem is solved by initiating the chemical reactions within the droplets and allowing the system to reach a steady-state; in effect, we are annealing the effective spin system to its ground state. We propose two implementations of the idea, which we ordered in terms of increasing complexity. First, we introduce a hybrid classical-molecular computer where droplet properties are measured and fed into a classical computer. Based on the given optimization problem, the classical computer then directs further reactions via optical or electrochemical inputs. A simulated model of the hybrid classical-molecular computer is used to solve boolean satisfiability and a lattice protein model. Second, we propose architectures for purely molecular computers that rely on pre-programmed nearest-neighbour inter-droplet communication via energy or mass transfer.

scholarly journals A Molecular Computing Approach to Solving Optimization Problems via Programmable Microdroplet Arrays

2019 ◽  
Author(s):  
Si Yue Guo ◽  
Pascal Friederich ◽  
Yudong Cao ◽  
Tony Wu ◽  
Christopher Forman ◽  
...  
Keyword(s):  
Chemical Reactions ◽  
Optimization Problems ◽  
Research Direction ◽  
Molecular Computing ◽  
Effective Spin ◽  
Combinatorial Optimization Problems ◽  
Protein Model ◽  
Computer Based ◽  
Classical Computer ◽  
Molecular Computers

The search for novel forms of computing that show advantages as alternatives to the dominant von-Neuman model-based computing is important as it will enable different classes of problems to be solved. By using droplets and room-temperature processes, molecular computing is a promising research direction with potential biocompatibility and cost advantages. In this work, we present a new approach for computation using a network of chemical reactions taking place within an array of spatially localized droplets whose contents represent bits of information. Combinatorial optimization problems are mapped to an Ising Hamiltonian and encoded in the form of intra- and inter- droplet interactions. The problem is solved by initiating the chemical reactions within the droplets and allowing the system to reach a steady-state; in effect, we are annealing the effective spin system to its ground state. We propose two implementations of the idea, which we ordered in terms of increasing complexity. First, we introduce a hybrid classical-molecular computer where droplet properties are measured and fed into a classical computer. Based on the given optimization problem, the classical computer then directs further reactions via optical or electrochemical inputs. A simulated model of the hybrid classical-molecular computer is used to solve boolean satisfiability and a lattice protein model. Second, we propose architectures for purely molecular computers that rely on pre-programmed nearest-neighbour inter-droplet communication via energy or mass transfer.

scholarly journals A Soft Computing Approach for Selecting and Combining Spectral Bands

2020 ◽  
Vol 12 (14) ◽  
pp. 2267
Author(s):  
Juan F. H. Albarracín ◽  
Rafael S. Oliveira ◽  
Marina Hirota ◽  
Jefersson A. dos Santos ◽  
Ricardo da S. Torres
Keyword(s):  
Remote Sensing ◽  
Soft Computing ◽  
Optimization Problems ◽  
Multispectral Images ◽  
Spectral Indices ◽  
Vegetation Types ◽  
Classification Problems ◽  
Spectral Bands ◽  
Classification Tasks ◽  
Computing Approach

We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learned spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes.

A reservoir computing approach for molecular computing

2018 ◽  
Author(s):  
Wataru Yahiro ◽  
Nathanael Aubert-Kato ◽  
Masami Hagiya
Keyword(s):  
Molecular Computing ◽  
Reservoir Computing ◽  
Computing Approach

A Molecular Solution to the Three-Partition Problem

2012 ◽  
Vol 5 (4) ◽  
pp. 14-29
Author(s):  
Maryam S. Nuser
Keyword(s):  
Molecular Computing ◽  
Partition Problem ◽  
Biomolecular Computing ◽  
Hard Problems ◽  
Np Complete ◽  
Computing Approach

Given a set of numbers, the three-partition problem is to divide them into disjoint triplets that all have the same sum. The problem is NP-complete. This paper presents an algorithm to solve this problem using the biomolecular computing approach. The algorithm uses a distinctive encoding technique that depends on the numbers values which omits the need to an adder to find the sum. The algorithm is explained and an analysis of its complexity in terms of time, the number of strands, number of tubes, and the longest library strand used is presented. A simulation of the algorithm is implemented and tested. This algorithm further proves the ability of molecular computing in solving hard problems.

scholarly journals On Implementing Autonomic Systems with a Serverless Computing Approach: The Case of Self-Partitioning Cloud Caches

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Edwin F. Boza ◽  
Xavier Andrade ◽  
Jorge Cedeno ◽  
Jorge Murillo ◽  
Harold Aragon ◽  
...  
Keyword(s):  
Memory Management ◽  
Optimization Problems ◽  
Cost Savings ◽  
Cloud Services ◽  
Autonomic Systems ◽  
Self Tuning ◽  
The Cost ◽  
Software Caches ◽  
Computing Approach

The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory management in the multi-instance Redis scenario. A cost analysis shows that the serverless computing approach can lead to significant cost savings: The cost of running the controller as a serverless microservice is 0.85% of the cost of the always-on alternative. Through this case study, we make a strong case for implementing the controller of autonomic systems using a serverless computing approach.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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