Discrete Genetic Algorithm Research Articles

Discrete Optimization on Unsteady Pressure Fluctuation of a Centrifugal Pump Using ANN and Modified GA

Pressure fluctuation due to rotor-stator interaction in turbomachinery is unavoidable, inducing strong vibration in the equipment and shortening its lifecycle. The investigation of optimization methods for an industrial centrifugal pump was carried out to reduce the intensity of pressure fluctuation to extend the lifecycle of these devices. Considering the time-consuming transient simulation of unsteady pressure, a novel optimization strategy was proposed by discretizing design variables and genetic algorithm. Four highly related design parameters were chosen, and 40 transient sample cases were generated and simulated using an automatic program. 70% of them were used for training the surrogate model, and the others were for verifying the accuracy of the surrogate model. Furthermore, a modified discrete genetic algorithm (MDGA) was proposed to reduce the optimization cost owing to transient numerical simulation. For the benchmark test, the proposed MDGA showed a great advantage over the original genetic algorithm regarding searching speed and effectively dealt with the discrete variables by dramatically increasing the convergence rate. After optimization, the performance and stability of the inline pump were improved. The efficiency increased by more than 2.2%, and the pressure fluctuation intensity decreased by more than 20% under design condition. This research proposed an optimization method for reducing discrete transient characteristics in centrifugal pumps.

Modeling and optimizing in vitro percentage and speed callus induction of carrot via Multilayer Perceptron-Single point discrete GA and radial basis function

BackgroundCallus induction is the first step in optimizing plant regeneration. Fit embryogenesis and shooting rely on callus induction. In addition, using artificial intelligence models in combination with an algorithm can be helpful in the optimization of in vitro culture. The present study aimed to evaluate the percentage and speed of callus induction optimization in carrot with a Multilayer Perceptron-Single point discrete genetic algorithm (GA).Materials and methodsIn this study, the outputs included callus induction percentage and speed, while inputs were different types and concentrations of plant growth regulator (0. 5, 0.2 mg/l 2,4-D, 0.3, 0.2, 0.5 mg/l BAP, 1, 0.2 mg/l Kin, and 2 mg/l NAA), different explants (shoot, root, leaf, and nodal), a different concentration compound of MS medium (1 × MS, 4× MS, and 8× MS) and time of sampling. The data were obtained in the laboratory, and multilayer perceptron (MLP) and radial basis function (RBF), two well-known ANNs, were employed to model. Then, GA was used for optimization, and sensitivity analysis was performed to indicate the inputs’ importance.ResultsThe results showed that MLP had better prediction efficiency than RBF. Based on the results, R2 in training and testing data was 95 and 95% for the percentage of callus induction, while it was 94 and 95% for the speed of callus induction, respectively. In addition, a concentration compound of MS had high sensitivity, while times of sampling had low sensitivity. Based on the MLP-Single point discrete GA, the best results were obtained for shoot explants, 1× MS media, and 0.5 mg/l 2, 4-D + 0.5 mg/l BAP. Further, a non-significant difference was observed between the test result and predicted MLP.ConclusionsGenerally, MLP-Single point discrete GA is considered a potent tool for predicting treatment and fit model results used in plant tissue culture and selecting the best medium for callus induction.

Adaptive path planning for the gantry welding robot system

Group welding is one of the crucial components with difficulties in shipbuilding with irregular distribution and a great variety of welding seams. The traditional manual teaching method has drawbacks of the long development period, low efficiency, and poor welding accuracy in dealing with such weld seams in the welding process. In order to realize automation planning and welding for arc welding robot, a gantry welding robot system with adaptive path planning ability is proposed to solve the problem with various welding workpieces and position. The 3D laser scanning sensor is first adopted to identify the type and position of the workpiece. Then, the discrete genetic algorithm based on inverted pyramid structure (GA-IPS) is applied for welding sequence planning, the discrete two-stage adaptive reference direction guided evolutionary algorithm with modified dominance relation for many-objective optimization (AREA-APD) is proposed for dual-objective welding sequence planning. Furthermore, the lazy probabilistic roadmap method with transition points (Lazy PRM-TP) is applied to local trajectory planning. And the laser vision system is used to realize the precise location and tracking of the welding seam. Besides, the computer system is designed to realize human-computer interaction and process monitoring. The simulation and experiments show the superior performance of the proposed strategy.

Multi-Agent Task Allocation Based on Discrete DEPSO in Epidemic Scenarios

Multi-Agent Task Allocation is an emerging technology that changes the world in the epidemic scenario through its power to serve the needs of any hospital that requires unmanned operation. In this environment, the end user may want to have a better quality of unmanned service at low loss and high efficiency. We defined a new multi-agent task allocation problem (MATAP) in the epidemic scenario, and then MATAP was formulated. This paper presents a novel hybrid discrete approach that is based on the Differential Evolution Algorithm (DE) and Partial Swarm Optimization (PSO), namely D-DEPSO, for handling this problem. First, the initial personal population was handled by "mutation operation". Modulus operations in the "mutation operation" modify the numerical overflow of a variable. Second, when updating the speed matrix, the speed matrix is discretized using the "round" function we have defined. Then, a random permutation was used to delete repeated numbers and to reinsert integers in the "crossover operation". The diversity of the population was expanded by introducing the discrete mutation operation of the DE into the PSO and preserving the optimal solution for each generation using the properties of PSO. It can be used for optimizing a single objective function. Experimental results are compared with other existing metaheuristic algorithms, such as discrete DE, discrete PSO, improved discrete DE, improved discrete PSO, and improved discrete genetic algorithm, in terms of running time and loss. The experiments show that the optimal solutions obtained by D-DEPSO are better than those obtained by other five algorithms. For the actual problem, D-DEPSO can generate an optimal solution by optimal parameter setting to allocate tasks rationally. It can achieve a rational distribution of tasks in the prevention of disease.

A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem

Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept of map segmentation which decomposes the problem of multiple geographical feature label placement (MGFLP) to achieve a more intuitive solution. Parallel computing is then utilized to handle each decomposed problem simultaneously on a separate central processing unit (CPU) to speed up the process of label placement. The optimization component of the proposed algorithm is designed based on the hybrid of discrete differential evolution and genetic algorithms. Our results based on real-world datasets confirm the usability and scalability of the algorithm and illustrate its excellent performance. Moreover, the algorithm gained superlinear speedup compared to the previous studies that applied this hybrid algorithm.

Optimization of flexible fixture layout to improve form quality using parametric finite element model and mixed discrete-integer genetic algorithm

Dimensional and form accuracy of the workpiece can be improved by effective fixture layout design which shows minimum deformation of the workpiece during machining. Flexible fixtures are inevitable in industries owing to high product variety and shortened production time. Hence, an integrated approach is presented to select the optimum position of locating and clamping elements in a flexible fixture that provide good form accuracy. In this approach, a Parametric Finite Element Model (PFEM) is developed using the information about the workpiece, fixture plan and machining condition. PFEM is used to predict the elastic deformation of the workpiece for the fixture layouts generated using a discrete Genetic Algorithm (GA) with mixed integer-discrete variables. The objective is to minimize the maximum deformation of the workpiece by optimizing fixture layouts. The stability of the workpiece and fixture system is ensured by implementing non-negative reaction force constraints in GA. The proposed approach is applied for a prismatic workpiece to carry out pocket milling operation. The significance of this work is to express the flexibility and computational effectiveness of PFEM to accommodate variation in the workpiece, machining condition and fixture plan while designing flexible fixtures. Further, it highlights a significant reduction in search space due to the use of discrete GA and stability constraint as it takes less objective function calculations. An experimental analysis is performed to study the effectiveness of the proposed approach. Therefore, the proposed approach provides a viable solution to the optimization problem in flexible fixtures.

A New Active Power Filter Partition Allocation for Distribution Networks

At present, the power quality compensation device configuration algorithm in distribution network is often very complex.Therefore, the dominant gene rules in nature are simulated and introduced to the algorithm.An optimal configuration model for harmonic compensation devices is established, which takes into account the weak coupling between different loads and compensation devices.Finally,the Implicit genetic rule of adaptive discrete genetic algorithm (IGADGA) gets rapid convergence and high precision, and the algorithm is validated by computer simulation.

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

Implementation and Analysis of Pattern Propagation Factor Based Radar Model for Path Planning

Various path planning algorithms assume space as free and obstacles, and it is widely used in the robotic field. In examples of flight objects, space cannot be simply divided as free and obstacles because a risk exposure factor in the sky is dramatically changed based on radar sites and earth terrain. Previous researchers did not consider the risk exposure or used simple radar model to estimate the risk exposure. In this paper, a radar model based on pattern propagation factor is implemented to estimate the risk exposure. The model can simulate effects of terrain masking, 3D radar cross-section, refraction, and radar multipath, and compared paths with deterministic (Dijkstra’s algorithm), evolutionary (Discrete Genetic Algorithm), and Voronoi path planning methods.

Risk management and design of mitigation plans through discrete events simulation and genetic algorithms in offshore wind processes

This paper tackles the difficult task of identifying risk and opportunities quantify them and propose mitigation plans that minimise the impact of the risk in manufacturing projects. For this reason, a discrete events simulation model representative of a manufacturing plant and manufacturing strategy has been developed. This simulation model will be able to identify and assess the risks of the project with regard to risk effect in the schedule or in the manufacturing cost and, on the other hand, simulation model will be used in combination with an optimiser based on genetic algorithms (GAs), allowing the design of new manufacturing strategies or new plant layouts. Finally, the experimental results were obtained customising the simulation model for a real project of manufacturing wind turbine foundations, in particular to the piles that hold the lattice structure (jacket) to the bottom of the sea.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ ЭФФЕКТИВНОСТИ АЛГОРИТМОВ РЕШЕНИЯ ЗАДАЧИ О РЮКЗАКЕ ДЛЯ ОПТИМИЗАЦИИ КОЛИЧЕСТВА ЗАЯВОК НА ИННОВАЦИИ

Цель: сравнительный анализ алгоритмов решения задачи отбора заявок на инновационные проекты в условиях ограниченного бюджета. Обсуждение: указанная проблема формализуется как задача о рюкзаке (ранце), для решения которой применяются методы дискретной оптимизации и генетические алгоритмы. Предлагается набор критериев эффективности алгоритмов и проводится соответствующий сравнительный анализ. Результаты: авторами программно реализованы алгоритмы дискретной оптимизации и генетические алгоритмы для решения задачи о рюкзаке применительно к отбору заявок на инновационные проекты, проведен анализ их сравнительной эффективности по ряду критериев, приведены результаты работы алгоритмов.

Trajectory Planning for Communication Relay Unmanned Aerial Vehicles in Urban Dynamic Environments

This paper proposes an optimal positioning and trajectory planning algorithm for unmanned aerial vehicles (UAVs) to improve a communication quality of a team of ground mobile nodes (vehicles) in a complex urban environment. In particular, a nonlinear model predictive control (NMPC)-based approach is proposed to find an efficient trajectory for UAVs with a discrete genetic algorithm while considering the dynamic constraints of fixed-wing UAVs. The advantages of using the proposed NMPC approach and the communication performance metrics are investigated through a number of scenarios with different horizon steps in the NMPC framework, the number of UAVs used, heading rates and speeds.

Optimisation of Offshore Wind Farms Using a Genetic Algorithm

A modular framework for the optimisation of an offshore wind farm using a discrete genetic algorithm is presented. This approach uses a bespoke grid generation algorithm to define the discrete positions that turbines may occupy thereby implicitly satisfying navigational and search and rescue constraints through the wind farm. The presented methodology takes a holistic approach optimising both the turbine placement and inter-array cable network, while minimising the levelised cost of energy and satisfying real world constraints. This tool therefore integrates models for the assessment of the energy production including wake losses; the optimisation of the inter-array cables; and the estimation of costs of the project over the lifetime. This framework will allow alternate approaches to wake and cost modelling as well as optimisation to be benchmarked in the future.

Optimal daily scheduling of reconfiguration based on minimisation of the cost of energy losses and switching operations in microgrids

Network reconfiguration has been involved in energy loss reduction by considering the time-varying nature of loads. Nowadays, with the integration of intermittent micro-sources (MSs) to the power grid, optimal configuration should be determined corresponding to the variations in loads and generations. In this study, optimum reconfiguration instants are achieved based on the switching operations and energy losses costs. Moreover, the sequence of selecting loops for reconfiguration is optimised in conjunction with the time intervals by a joint optimisation algorithm. Furthermore, a simple branch exchange method of reconfiguration is modified in the presence of MSs. Discrete genetic algorithm (DGA) is employed to optimise the reconfiguration instants as well as the sequence of selecting loops. By applying the proposed method, the total operating cost of the network is reduced significantly, compared to optimum fixed configuration and hourly reconfiguration policies. The proposed algorithm is evaluated on a real-life 77-bus distribution system with two interconnected MSs.

HSQC spectral based similarity matching of compounds using nearest neighbours and a fast discrete genetic algorithm

HSQC spectra are routinely acquired for chemical structure analysis based on hydrogen and carbon chemical environments. Two fast HSQC peak matching algorithms have been developed; a nearest neighbour approach and a probabilistic method based on an existing discrete genetic algorithm. Both of these techniques are intended to find HSQC spectra matches that supplement information generated by established molecular fingerprint methods. Our results are compared to those calculated using a specific implementation of molecular fingerprints. The nearest neighbour and genetic algorithm-based methods ranked highly particular structures missed by molecular fingerprints. Our analysis shows that by complementing molecular fingerprint matches with our findings, a comprehensive list of matches can be identified. The refined list of compounds could be used to improve the quality of compounds used in screening libraries in the pharmaceutical industry.

Reconfiguration and Capacitor Placement Simultaneously for Energy Loss Reduction Based on an Improved Reconfiguration Method

Network reconfiguration and capacitor placement have been widely employed to reduce power losses and maintain voltage profiles within permissible limits in distribution systems. Reconfiguration method proposed in this paper is based on a simple branch exchange method of single loop. In this simple method of branch exchange, loops selection sequence affects the optimal configuration and the network loss. Therefore, this method has been improved by optimizing the sequence of loops selection for minimizing the energy losses in this paper. Also, a joint optimization algorithm is proposed for combining this improved method of reconfiguration and capacitor placement and therefore maximum loss reduction. For more practical application of the proposed method, different load patterns are considered and a fast method of total energy loss calculation is employed for the economic optimization of energy losses during the planning horizon. Discrete genetic algorithm (GA) is used to optimize the location and size of capacitors and the sequence of loops selection. In fact, the capacitor sizes have been considered as discrete variables. Simulated annealing (SA) is also applied to compare the performance of convergence. The proposed algorithm is effectively tested on a real life 77-bus distribution system with four different kinds of load patterns.

Improved discrete PSO algorithm and its application in winner determination problem

A kind of discrete Particle Swarm Optimization (PSO) algorithm named NDPSO was proposed for extending the classic PSO model to solve the discrete optimization problems with high effectiveness and stability. The concept of comparison with probability was also introduced based on NDPSO, and then the stochastic repairing operator was constructed for heuristic search to solve the Winner Determination Problem (WDP) in combinatory auction. The experimental results show that NDPSO has great advantages in both success rate and convergence speed compared with other discrete PSO algorithms and genetic algorithm.

An optimization technique using the characteristics of genetic algorithm

AbstractOptimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient‐based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.