QPSO‐Based Adaptive DNA Computing Algorithm

Abstract

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm.

Parallel chaos optimization algorithm with migration and merging operation

“…Population size is one of the most significant characteristics that contribute to the solution of the problem [27]. Like the population size to evolutionary computing, the parallel number N in the MMO-PCOA is one of parameters that may effect the search performance.…”

Model Checking Temporal Logic Formulas Using Sticker Automata

“…Some of the major studies are summarized as follows: (1) aiming to deal with some inherent flaws of DNA computing, such as adaptability [31] and instability [32]; (2) employing DNAs to realize some basic computing components and/or techniques, such as data storage [33], database operations [34], odd parity checker [35], half adder [36], encryption [37], and data hiding [38]; (3) utilizing DNAs to address some problems in real world, such as the inverse kinematics redundancy problem of six-degree-of-freedom humanoid robot arms [39], dynamic control of elevator systems [40], and hyperspectral remote sensing data/imagery [41, 42]. …”

Hybrid Taguchi DNA Swarm Intelligence for Optimal Inverse Kinematics Redundancy Resolution of Six-DOF Humanoid Robot Arms

“…This algorithm proposed by Aldelman [17] emulates the concept of the bimolecular evolution and uses biomolecules for finding optimal solutions of complicated computational problems. This computing paradigm has successfully been used to solve complex problems in many disciplines [17][18][19][20] because they have more plentiful genetic information [17][18][19][20]. However, the parameters of the proposed DNA swarm intelligence algorithms are usually determined by trial-and-error approach [17][18][19][20].…”

“…These parameters influence the performance of the DNA algorithms. Unfortunately, the parameters are not appropriately set in the studies to solve complex optimization problems [17][18][19][20].…”