A Parallel Biological Optimization Algorithm to Solve  the Unbalanced Assignment Problem Based on  DNA Molecular Computing

Wang, Zhaocai; Pu, Juan; Cao, Liling; Tan, Jian

doi:10.3390/ijms161025338

Cited by 12 publications

(4 citation statements)

References 30 publications

(42 reference statements)

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“…rough different operation and control technologies for DNA molecules, new algorithms can be formed to provide strong technical support for many problems without effective solutions [26][27][28][29][30][31][32][33][34][35][36][37][38][39]. For the Chinese postman problem, with the increase of data volume and scale, the traditional algorithm will be more difficult to solve it.…”

Section: Biocomputing and Modelingmentioning

confidence: 99%

A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

Wang

Bao

2021

Computational Intelligence and Neuroscience

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The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems.

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Section: Biocomputing and Modelingmentioning

confidence: 99%

A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

Wang

Bao

2021

Computational Intelligence and Neuroscience

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“…e bases of the genetic algorithm for solving the optimal maintenance strategy of PV plants are as follows: First, the genetic algorithm solves various TSP problems with many results [22][23][24] and is mature and reliable. Second, the genetic algorithm satisfies the solution of different problems; for instances, the problem of multipoint departure and multiperson operation and maintenance can be assigned by adjusting the chromosome coding method, and the operation and maintenance strategy with uncertain time constraints and cost calculation methods can be made by adjusting the fitness function.…”

Section: Optimization Algorithm Of Maintainers Dispatchingmentioning

confidence: 99%

Maintenance and Operation Optimization Algorithm of PV Plants under Multiconstraint Conditions

Hua

Kuang²,

2020

Complexity

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With the rapid increase in the photovoltaic (PV) plants, the real-time operation and maintenance of photovoltaic power generation equipment is very important. The maintenance and dispatching of decentralized power stations is still one of the key issues affecting the operation safety of photovoltaic power stations. However, most of the photovoltaic power stations in China fail to rationally optimize the utilization of resources and time. The current study puts forward effort implementation via genetic algorithm-based multiconstrained optimization methodology. The proposed study optimally overrides the traditional PV plant operation and maintenance dispatching operations with automation and reliability. The proposed study is also applicable to multiple PV plants, multiple maintainers, multipoint departure, different dispatching conditions, and cost considerations. We propose an MOOA algorithm to solve this issue, and we strongly believe that, by defining a suitable fitness function, the convergence speed and optimization ability can be greatly improved, and this study puts a forward step.

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“…Jia et al [36] presented several heuristics for P r j , s j , p − batch, K i C max (the problem where jobs have different release times) and evaluated the validity of the heuristics by computational experiments. Other methods have also been proposed in the literature [42][43][44][45][46][47][48][49][50][51][52][53].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

Zhang

Song

et al. 2020

Applied Sciences

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With the rapid economic development, manufacturing enterprises are increasingly using an efficient workshop production scheduling system in an attempt to enhance their competitive position. The classical workshop production scheduling problem is far from the actual production situation, so it is difficult to apply it to production practice. In recent years, the research on machine scheduling has become a hot topic in the fields of manufacturing systems. This paper considers the batch processing machine (BPM) scheduling problem for scheduling independent jobs with arbitrary sizes. A novel fast parallel batch scheduling algorithm is put forward to minimize the makespan in this paper. Each of the machines with different capacities can only handle jobs with sizes less than the capacity of the machine. Multiple jobs can be processed as a batch simultaneously on one machine only if their total size does not exceed the machine capacity. The processing time of a batch is determined by the longest of all the jobs processed in the batch. A novel and fast 4.5-approximation algorithm is developed for the above scheduling problem. For the special case of all the jobs having the same processing times, a simple and fast 2-approximation algorithm is achieved. The experimental results show that fast algorithms further improve the competitive ratio. Compared to the optimal solutions generated by CPLEX, fast algorithms are capable of generating a feasible solution within a very short time. Fast algorithms have less computational costs.

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A Parallel Biological Optimization Algorithm to Solve the Unbalanced Assignment Problem Based on DNA Molecular Computing

Cited by 12 publications

References 30 publications

A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

Maintenance and Operation Optimization Algorithm of PV Plants under Multiconstraint Conditions

A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem

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