mRNA codon optimization with quantum computers

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.

Classification Method of Uniform Circular Array Radar Ground Clutter Data Based on Chaotic Genetic Algorithm

The classification and recognition of radar clutter is helpful to improve the efficiency of radar signal processing and target detection. In order to realize the effective classification of uniform circular array (UCA) radar clutter data, a classification method of ground clutter data based on the chaotic genetic algorithm is proposed. In this paper, the characteristics of UCA radar ground clutter data are studied, and then the statistical characteristic factors of correlation, non-stationery and range-Doppler maps are extracted, which can be used to classify ground clutter data. Based on the clustering analysis, results of characteristic factors of radar clutter data under different wave-controlled modes in multiple scenarios, we can see: in radar clutter clustering of different scenes, the chaotic genetic algorithm can save 34.61% of clustering time and improve the classification accuracy by 42.82% compared with the standard genetic algorithm. In radar clutter clustering of different wave-controlled modes, the timeliness and accuracy of the chaotic genetic algorithm are improved by 42.69% and 20.79%, respectively, compared to standard genetic algorithm clustering. The clustering experiment results show that the chaotic genetic algorithm can effectively classify UCA radar’s ground clutter data.

Route Selection and Distribution Cost of Express Delivery: An Urban Metro Network Based Study

Route selection and distribution costs of express delivery based on the urban metro network, referred to as metro express delivery (MeD), is addressed in this study. Considering the characteristics of express delivery transportation and the complexity of the urban metro network, three distribution modes of different time periods are proposed and a strict integrated integer linear programming model is developed to minimize total distribution costs. To effectively solve the optimal problem, a standard genetic algorithm was improved and designed. Finally, the Ningbo subway network is used as an example to confirm the practicability and effectiveness of the model and algorithm. The results show that when the distribution number of express delivery packages is 1980, the three different MeD modes can reduce transportation costs by 40.5%, 62.0%, and 59.0%, respectively. The results of the case analysis will help guide express companies to collaborate with the urban metro network and choose the corresponding delivery mode according to the number of express deliveries required.

mRNA codon optimization on quantum computers

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. An adiabatic quantum computer (AQC) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The AQC is found to be competitive in identifying optimal solutions and future generations of AQCs may be able to outperform classical GAs. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.

Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm

In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such as premature maturity, low convergence path quality, poor population diversity, and difficulty in breaking the local optimal solution, this paper proposes a multi-population migration genetic algorithm. The multi-population migration genetic algorithm randomly divides a large population into several small with an identical population number. The migration mechanism among the populations is used to replace the screening mechanism of the selection operator. Operations such as the crossover operator and the mutation operator also are improved. Simulation results show that the multi-population migration genetic algorithm (MPMGA) is not only suitable for simulation maps of various scales and various obstacle distributions, but also has superior performance and effectively solves the problems of the standard genetic algorithm.

Network Reconfiguration of Radial Active Distribution Systems in Uncertain Environment Using Super Sense Genetic Algorithm

Enormous work has been reported in literature to enhance the performance of metaheuristics by modifying their internal mechanisms via intervening their control equations. Usually, these population based techniques are initiated through random creation of individuals (tentative solutions) to preserve adequate diversity in population and then attempts have been made to maintain a better balance between exploration and exploitation of the problem search space. However, it would be much better if some strategy is employed that could divert tentative solutions toward the promising region. This can be possible if the algorithm has some mechanism to develop certain knowledge (super sense) about the quality of decision variables of the problem. This paper presents super sense genetic algorithm (SSGA) that gradually develops super sense during successive genetic evolutions. The accumulated genetic information so obtained is stored and used to divert individuals near the promising region while preserving adequate diversity. SSGA differs to standard genetic algorithm (GA) only on this aspect. SSGA is applied to solve complex combinatorial network reconfiguration problem of radial distribution systems. The application results highlight the effectiveness of proposed GA.

Design and control of electrotechnological systems

Purpose This paper aims to investigate different multi-objective optimization (MOO) approaches for design and control of electromagnetic devices. The main goal of MOO is to find the set of design variables or control parameters which will provide the best possible values of typical conflicting objective functions. Design/methodology/approach In the research studies, standard genetic algorithm (GA), non-dominated sorting GA (NSGA-II), migration NSGA algorithm and alternance method of optimal control theory are discussed and compared. Findings The test practical problems of multi-criteria optimization of induction heating processes with respect to chosen quality criteria confirm the effectiveness of application of considered MOO approaches both for the problems of design and control. Originality/value This paper represents and investigates different MOO approaches for design and control of electrotechnological systems.

Study on batching and picking optimization of marine outfitting pallets

Outfitting pallet picking involves the retrieval of items from their storage sites in shipbuilding enterprises. A major issue in manual pallet picking operations is the transformation of outfitting pallets into picking batches (pallet batching). Considering the influence to the subsequent distribution and production processes, a mathematical model for the batching and picking problem of outfitting pallets is formulated with the objective of minimizing the total tardiness of all pallets. According to the characteristics of outfitting pallet picking operations, an Improved Genetic Algorithm (IGA) is proposed. A reversal operator is specially introduced to increase the local search ability of the standard genetic algorithm and speed up the evolution. Benchmarked against the solutions produced by the Earliest Due Date (EDD) rule, the performance of IGA is studied under different picking operations with different workloads and tightness of due dates. A series of numerical experiments are carried out to verify the researches. The results clearly show that IGA is competitive since it improves the solutions by 68.5%, on average, relative to the EDD.

OPTIMASI PENENTUAN LOKASI DAN KELUARAN DAYA AKTIF PEMBANGKIT KECIL TERSEBAR METODE ALGORITMA GENETIKA STANDAR PADA PENYULANG 1 SISTEM DISTRIBUSI 20 KV TARAKAN

- The use of distributed generation (DG) at Feeder I Tarakan Distribution System 20 kV aims to meet the needs of consumer electricity, but it is expected to reduce the loss of power on the network. Therefore, optimization is needed in determining the location and output of DG’s active power. The standard genetic algorithm method is used in the determination of the location and output of active power for 3 units of each 250 kW capacity in Feeder I Tarakan Distribution System 20 kV. The optimized results were achieved for optimal locations on 22,28, 47 buses with each 196, 192, 200 kW active power output and 37.73 % or 17,000 Watt network loss. Keywords: standard genetic algorithm, electrical distribution system, distributed generation Intisari - Pemanfaatan pembangkit kecil tersebar (PKT) pada Penyulang I Sistem Distribusi Listrik Tarakan 20 kV bertujuan untuk pemenuhan kebutuhan listrik konsumen dan diharapkan dapat meminimalkan rugi daya pada jaringan. Untuk itu diperlukan optimasi dalam menentukan lokasi dan keluaran daya aktif PKT. Metode algoritma genetika standar digunakan dalam penentuan lokasi dan keluaran daya aktif untuk 3 Unit PKT kapasitas masing-masing 250 kW pada Penyulang I Sistem Distribusi 20 KV Tarakan. Hasil optimasi yang dicapai untuk lokasi optimal pada bus 22, 28, 47 dengan masing-masing keluaran daya aktif 196, 192, 200 kW dan penurunan rugi daya jaringan sebesar 37,73 % atau 17.000 Watt. Kata Kunci : algoritma genetika standar, sistem distribusi listrik, pembangkit kecil tersebar