DNA Sequence Design for Direct-Proportional Length-Based DNA Computing: Particle Swarm Optimization vs Population Based Ant Colony Optimization

2012 ◽  
Author(s):  
Zulkifli Md. Yusof ◽  
Muhammad Arif Abdul Rahim ◽  
Sophan Wahyudi Nawawi ◽  
Kamal Khalil ◽  
Zuwairie Ibrahim ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Dna Sequence ◽  
Dna Computing ◽  
Particle Swarm ◽  
Population Based ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Sequence Design ◽  
Dna Sequence Design

Improved Multi-Objective Particle Swarm Optimization Algorithm for DNA Sequence Design

2020 ◽  
Vol 15 (12) ◽  
pp. 1450-1459
Author(s):  
Ying Niu ◽  
Hangyu Zhou ◽  
Shida Wang ◽  
Kai Zhao ◽  
Xiaoxiao Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Experimental Simulation ◽  
Swarm Optimization ◽  
Sequence Design ◽  
Multi Objective ◽  
Dna Sequence Design

The DNA sequence design is a vital step in reducing undesirable biochemical reactions and incorrect computations in successful DNA computing. To this end, many studies had concentrated on how to design higher quality DNA sequences. However, DNA sequences involve some thermodynamic and conflicting conditions, which in turn reflect the evolutionary algorithm process implemented through chemical reactions. In the present study, we applied an improved multi-objective particle swarm optimization (IMOPSO) algorithm to DNA sequence design, in which a chaotic map is combined with this algorithm to avoid falling into local optima. The experimental simulation and statistical results showed that the DNA sequence design method based on IMOPSO has higher reliability than the existing sequence design methods such as traditional evolutionary algorithm, invasive weed algorithm, and specialized methods.

Function Minimization In DNA Sequence Design Based On Binary Particle Swarm Optimization

2012 ◽  
Author(s):  
Zuwairie Ibrahim ◽  
Noor Khafifah Khalid ◽  
Ismail Sheng ◽  
Salinda Buyamin ◽  
Zulkifli Md. Yusof ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Melting Temperature ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Sequence Design ◽  
Dna Sequence Design

In DNA based computation and DNA nanotechnology, the design of good DNA sequences has turned out to be an elementary problem and one of the most practical and important research topics. Although the design of DNA sequences is dependent on the protocol of biological experiments, it is highly required to establish a method for the systematic design of DNA sequences, which could be applied to various design constraints. Basically, the fitness of DNA sequences can be evaluated using four objective functions, namely, similarity, Hmeasure, continuity and hairpin. In this paper, binary particle swarm optimization (BinPSO) is proposed to minimize those objective functions individually, subjected to two constraints: melting temperature and GCcontent. An implementation of the optimization process is presented using 20 particles and the results obtained shows the correctness of PSO computation, where the minimized values for each objective can be achieved. Kata kunci: DNA sequence design; binary particle swarm optimization; objective function; constraints; fitness function Dalam DNA berasaskan pengiraan dan nanoteknologi DNA, reka bentuk jujukan–jujukan DNA baik telah menjadi satu masalah asas dan salah satu topik–topik penyelidikan penting dan paling praktikal. Walaupun reka bentuk DNA berjujukan adalah bergantung kepada pada protokol eksperimen biologi, ia amat diperlukan bagi mewujudkan satu kaedah untuk reka bentuk bersistem DNA berjujukan, yang boleh digunakan untuk pelbagai reka bentuk kekangan. Pada asasnya, kecergasan DNA berjujukan boleh dinilaikan menggunakan empat fungsi–fungsi objektif, iaitu, similarity, Hmeasure, continuity dan hairpin. Dalam kertas kerja ini, pengoptimuman kerumunan zarah perduaan (BinPSO) dicadangkan untuk meminimumkan fungsi–fungsi objektif tersebut secara individu, tertakluk kepada dua kekangan: melting temperature dan GCcontent Satu perlaksanaan proses pengoptimuman dibentangkan menggunakan 20 zarah–zarah dan keputusan yang diperolehi menunjukkan kebenaran pengiraan PSO, di mana nilai–nilai yang minimum untuk setiap objektif dapat dicapai. Key words: Reka bentuk jujukan DNA; pengoptimuman kerumunan zarah perduaan; fungsi objektif; kekangan; fungsi kecergasan

scholarly journals QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

2021 ◽  
Vol 14 (1) ◽  
pp. 270-280
Author(s):  
Abhijit Halkai ◽  
◽  
Sujatha Terdal ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Large Scale ◽  
Cluster Head ◽  
Particle Swarm ◽  
Base Station ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Localization Model

A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem

2009 ◽  
Vol 626-627 ◽  
pp. 717-722 ◽  
Author(s):  
Hong Kui Feng ◽  
Jin Song Bao ◽  
Jin Ye
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Particle Swarm ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Great Success ◽  
Swarm Optimization ◽  
Continuous Space ◽  
Multiple Traveling Salesman Problem

A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.

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