Solving the Family Traveling Salesperson Problem in the Adleman–Lipton Model Based on DNA Computing

Wu, Xian; Wang, Zhao Cai; Wu, Tun Hua; Bao, Xiao Guang

doi:10.1109/tnb.2021.3109067

Cited by 27 publications

(11 citation statements)

References 42 publications

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“…Currently, DNA computing algorithms for different complex problems are being proposed, for example, Wu et al [ 48 ] and Tian et al [ 31 ] used DNA computing to solve the family traveling salesperson problem and job shop scheduling problem respectively, achieving great efficiency gains in terms of algorithmic computational complexity. In addition, DNA computing has been increasingly applied to different scenarios, such as image recognition [ 53 ], artificial neural network design [ 54 ] and quantum computing [ 55 ].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it is necessary to design DNA sequences suitable for simulation experiments. A Python program was designed to perform the simulation experiments, and the similar approach have been used in previous studies [ 48 ]. The computer used for the simulations has an AMD Ryzen 7 PRO 4750U processor with a clock speed of 1.70 GHz, Windows 10, 64 bit and 16G of RAM.…”

Section: Simulation Experiments Of Dna Algorithmmentioning

confidence: 99%

“…Proof. Set l � 􏽐 􏽐 (i, j) and the length of the different strands is: 41,8,5,53,58,24,35,11,48,38,1,39,28,33,20,37,26,55,43,10,56,22,18,23,54,44,3,50,27,42,6,34,19,14,25,51,36,59,49,45, 0] 60 [0, 34,3,26,23,58,53,5,18,1,9,16,8,…”

Section: Proofmentioning

confidence: 99%

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A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model

Wang

2022

Computational Intelligence and Neuroscience

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The quota traveling salesman problem (QTSP) is a variant of the traveling salesman problem (TSP), which is a classical optimization problem. In the QTSP, the salesman visits some of the n cities to meet a given sales quota Q while having minimized travel costs. In this paper, we develop a DNA algorithm based on Adleman-Lipton model to solve the quota traveling salesman problem. Its time complexity is O n 2 + Q , which is a significant improvement over previous algorithms with exponential complexity. A coding scheme of element information is pointed out, and a reasonable biological algorithm is raised by using limited conditions, whose feasibility is verified by simulation experiments. The innovation of this study is to propose a polynomial time complexity algorithm to solve the QTSP. This advantage will become more obvious as the problem scale increases compared with the algorithm of exponential computational complexity. The proposed DNA algorithm also has the significant advantages of having a large storage capacity and consuming less energy during the operation. With the maturity of DNA manipulation technology, DNA computing, as one of the parallel biological computing methods, has the potential to solve more complex NP-hard problems.

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Section: Discussionmentioning

confidence: 99%

Section: Simulation Experiments Of Dna Algorithmmentioning

confidence: 99%

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A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model

Wang

2022

Computational Intelligence and Neuroscience

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“…To validate the performance of MSACLPSO, the PSO, BLPSO, CLPSO, CPMPSO, IJAYA, GOTLBO, SATLBO, DE/BBO, DBBO, STLBO, WOA, CWOA, LWOA, GWO, EGWO, WDO, DE, JADE, and MPPCEDE [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] algorithms were used for comparison. The parameter values of MSACLPSO were the same as given in Section 5.2 .…”

Section: Case Analysismentioning

confidence: 99%

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

Zhang

Song

2022

Entropy

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In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-population parallel strategy is designed to improve population diversity and accelerate convergence. The population particle exchange and mutation are realized to ensure information sharing among the particles. Then, the global optimal value is added to velocity update to design a new velocity update strategy for improving the local search ability. The comprehensive learning strategy is employed to construct learning samples, so as to effectively promote the information exchange and avoid falling into local extrema. By linearly changing the learning factors, a new factor adjustment strategy is developed to enhance the global search ability, and a new adaptive inertia weight-adjustment strategy based on an S-shaped decreasing function is developed to balance the search ability. Finally, some benchmark functions and the parameter optimization of photovoltaics are selected. The proposed algorithm obtains the best performance on 6 out of 10 functions. The results show that the proposed algorithm has greatly improved diversity, solution accuracy, and search ability compared with some variants of particle swarm optimization and other algorithms. It provides a more effective parameter combination for the complex engineering problem of photovoltaics, so as to improve the energy conversion efficiency.

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“…Therefore, a large number of new optimization algorithms are constantly being proposed or improved. In recent decades, meta-heuristic algorithms based on the population have achieved good performance in solving complex engineering optimization problems: for example, the genetic algorithm (GA) [13], the simulated annealing algorithm (SAA) [14], particle swarm optimization (PSO) [15], the bat algorithm (BA) [16], ant colony optimization (ACO) [17], the novel moth to fire algorithm (MFO) [18], the locust optimization algorithm (GOA) [19], the butterfly optimization algorithm (BOA) [20], and the sine cosine optimization algorithm (SCA) [21], among others [22][23][24][25][26][27][28][29][30][31]. These algorithms can be grouped into two broad categories: individual-based and group-based.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Artificial Electric Field Algorithm with Sine Cosine Mechanism for Logistics Distribution Vehicle Routing

et al. 2022

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Aiming at the scheduling problem of logistics distribution vehicles, an enhanced artificial electric field algorithm (SC-AEFA) based on the sine cosine mechanism is proposed. The development of the SC-AEFA was as follows. First, a map grid model for enterprise logistics distribution vehicle path planning was established. Then, an enhanced artificial electric field algorithm with the sine cosine mechanism was developed to simulate the logistics distribution vehicle scheduling, establish the logistics distribution vehicle movement law model, and plan the logistics distribution vehicle scheduling path. Finally, a distribution business named fresh enterprise A in the Fuzhou Strait Agricultural and Sideline Products Trading Market was selected to test the effectiveness of the method proposed. The theoretical proof and simulation test results show that the SC-AEFA has a good optimization ability and a strong path planning ability for enterprise logistics vehicle scheduling, which can improve the scheduling ability and efficiency of logistics distribution vehicles and save transportation costs.

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Solving the Family Traveling Salesperson Problem in the Adleman–Lipton Model Based on DNA Computing

Cited by 27 publications

References 42 publications

A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model

A Parallel DNA Algorithm for Solving the Quota Traveling Salesman Problem Based on Biocomputing Model

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

An Enhanced Artificial Electric Field Algorithm with Sine Cosine Mechanism for Logistics Distribution Vehicle Routing

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