Dynamic Penalty Function Research Articles

The performance analysis of a new thermal backfill material for underground power cable system

Lafarge Gruntar™ material is proposed in this paper as a new thermal backfill component, and different mass fractions such as 5%, 10%, and 15% are examined. The thermal properties of the developed backfill are determined by experiments with respect to water content and time. Based on the experimental data it is found that the 15% mass fraction Lafarge Gruntar™ material provides the most favorable thermal conductivity i.e. over 1.00W/(mK) in the dry state. This paper also presents a thermal performance optimization procedure for the high voltage underground power cable system. The new thermal backfill material is considered to be installed within the cable system to improve its thermal performance. The analyzed system consists of three underground power cables situated in a flat formation (in−line arrangement) in thermal backfill, buried in the native soil. To avoid extensive mechanical loads caused by vibrations, when locating beneath a paved ground (e.g. under road crossings), the cables are situated in High Density Polyethylene (HDPE) casing pipes filled with sand−bentonite mixture. The pipes are then placed into a thermal backfill and buried in the native soil. The Installation of thermal backfill material is relatively expensive since in many cases the buried line is installed over many kilometers. Therefore, it is important to determine the optimal dimensions of the cable bedding layer to minimize the material and installation costs while keeping efficient heat dissipation from the cables. The momentum-type Particle Swarm Optimization (PSO) solver, with a dynamic penalty function, is used to minimize the cable backfill cross-sectional area while not exceeding the allowable temperature of the cable operation. The performed optimization procedure obtains the converged solution. The temperature distribution in soil, cables, and the cable backfill layer is determined using the Finite Element Method. The Campbell − de Vries thermal conductivity model is employed for the soil surrounding the underground power cable system. An MATLAB code is written for solving the heat conduction equation and to determine the temperature distribution within the underground power cable system. By using the momentum-type Particle Swarm Optimization algorithm, it is possible to design the best-found dimensions of the cable bedding layer (width and height). Moreover, the dynamic penalty function employed in the optimization procedure has assured the determination of the maximum temperature of cable conductor close to its best-found value.

The impact of land use constraints in multi-objective energy-noise wind farm layout optimization

Recently the environmental impact of onshore wind farms is receiving major attention from both governments and wind farm designers. As land is more extensively exploited for wind farms, it is more likely for wind turbines to be in proximity with human dwellings, infrastructure (e.g. roads, transmission lines), and natural habitats (e.g. rivers, lakes, forests). This proximity makes significant portions of land unusable for the designers, introducing a set of land-use constraints. In this study, we conduct a constrained and continuous-variable multi-objective optimization that considers energy and noise as its objective functions, based on Jensen's wake model and the ISO-9613-2 noise standard. A stochastic evolutionary algorithm (NSGA-II) solves the optimization problem, while the land-use constraints are handled with static and dynamic penalty functions. Results of this study illustrate the effect of constraint severity and spatial distribution of unusable land on the trade-off between energy generation and noise production.

Solving Portfolio Investment Model Based on Trade Data Stream in the Stock Market

Stock trading is a kind of modern economic movement in order to obtain high returns under risky investment activities. Stock value index only provides a tool for people with a measure of historical change of stock price. The paper presents a hybrid SAGA combined with dynamic penalty function algorithm is used to solve the model founded before. And also a more actual portfolio investment model, which is based on all requirements of the actual finical market such as no dividing stocks and all kinds of fees .The experiment results indicate that our algorithm has better searching ability and can avoid the occurring of earliness effectively.

A Route Planning’s Method for Micro UAV Based on Improved Genetic Algorithm

According to micro UAV is susceptible to wind environmental impact during the flight, this paper puts forward a kind of ergodicity track strategy for return path re planning. By adding coverage flight and sacrificing path to achieve safe recovery of voyage. A genetic algorithm is used to solve the problem, and introducing dynamic penalty function to improve the fitness function, effectively reduce the search space. The simulation results show that the method can generate safely track, and satisfy the path constraints.

Sensor Placement Optimization in Structural Health Monitoring Using Cluster-In-Cluster Firefly Algorithm

The determination of the optimal sensor placement (OSP) is a significant task that must be completed before a structural health monitoring (SHM) system is implemented on a real structure. The firefly algorithm (FA) is a recently developed nature-inspired metaheuristic algorithm for continuous optimization problems. This paper proposes a cluster-in-cluster firefly algorithm (CiCFA) for the optimum SHM sensor deployment. First, the code defining position coordinates in basic FA is replaced with a one-dimensional binary coding system, and the Euclidean distance is replaced by the Hamming distance. Then, a movement scheme for a darker firefly approaching a lighter firefly is developed. Last, a cluster-in-cluster strategy is employed to improve the convergence speed. In addition, a self-adaptive dynamic penalty function is introduced to convert the constraint imposed by the limited wireless data transmission range in the optimal wireless sensor placement (OWSP) problem to an unconstrained optimization problem. To demonstrate the effectiveness and applicability of the proposed optimization method, numerical examples of wired sensor placement and wireless sensor placement are carried out. The results reveal that the CiCFA provides a better sensor configuration than the genetic algorithm with high efficiency and high stability and can be applied in the OWSP problem. The proposed CiCFA provides a promising alternative for solving the OSP problem in practical SHM systems.

Wireless Sensor Placement for Bridge Health Monitoring Using a Generalized Genetic Algorithm

The optimal placement of wireless sensors is very different from conventional wired sensor placement due to the limited transmission range of the wireless sensors. This constraint on the inter-sensor distance makes the optimization problem difficult to solve with conventional gradient-based methods. In this paper, an improved generalized genetic algorithm (GGA) based on a self-adaptive dynamic penalty function (SADPF) is proposed for the optimal wireless sensor placement (OWSP) in bridge vibration monitoring. The mathematical model of the OWSP problem is established, and it considers both the bridge vibration monitoring requirements and the constraints of the data transmission range in wireless sensor networks (WSNs). SADPF, which can automatically adjust the amount of penalization for constraint violations according to the evolution generation number and the degree of violation, is then developed so that the wireless sensor placement can be optimized using GGA. Subsequently, the GGA is improved by implementing an elite conservation strategy, a worst elimination policy and a dual-structure coding system. Finally, a numerical experiment is presented with a long-span suspension bridge to demonstrate the feasibility and efficiency of the proposed method, and some indispensible discussions are also given. The results indicate that the wireless sensor configurations that are optimized by the improved SADPF-based GGA can simultaneously meet the data transmission demands in a WSN and fulfill the requirements for structural condition assessment. The developed SADPF can minimize the influence of the limited data transmission range on the search process for the OWSP. The improved SADPF-based GGA quickly and robustly converges to the global optimal solution.

The Optimization Design of Multistage Hybrid Planetary Transmission Based on Improved Genetic Algorithm

According to the characteristics of genetic algorithm, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is presented in this paper and it can overcome the disadvantages of genetic algorithm for improving the efficiency of algorithm. The improved genetic algorithm is applied to optimization design of multistage hybrid planetary transmission. It takes the minimum volumes as object functions, and fully considered such constraint condition. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. Therefore, the size parameters are optimized, as much as the driving stability and efficiency. Compared to the original program, the volume of 16.55% is decreased.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace

Developing better prediction models is crucial for the steelmaking industry to improve the continuous hot dip galvanising line (HDGL). This paper presents a genetic based methodology whereby a wrapper based scheme is optimised to generate overall parsimony models for predicting temperature set points in a continuous annealing furnace on an HDGL. This optimisation includes a dynamic penalty function to control model complexity and an early stopping criterion during the optimisation phase. The resulting models (multilayer perceptron neural networks) were trained using a database obtained from an HDGL operating in the north of Spain. The number of neurons in the unique hidden layer, the inputs selected and the training parameters were adjusted to achieve the lowest validation and mean testing errors. Finally, a comparative evaluation is reported to highlight our proposal’s range of applicability, developing models with lower prediction errors, higher generalisation capacity and less complexity than a standard method.

Optimal water and waste load allocation in reservoir–river systems: a case study

In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.

An adaptive parameter binary-real coded genetic algorithm for constraint optimization problems: Performance analysis and estimation of optimal control parameters

Real parameter constrained problems are an important class of optimization problems that are encountered frequently in a variety of real world problems. On one hand, Genetic Algorithms (GAs) are an efficient search metaheuristic and a prominent member within the family of Evolutionary Algorithms (EAs), which have been applied successfully to global optimization problems. However, genetic operators in their standard forms are blind to the presence of constraints. Thus, the extension of GAs to constrained optimization problems by incorporating suitable handing techniques is an active direction within GAs research. Recently, we have proposed a Binary Real coded Genetic Algorithm (BRGA). BRGA is a new hybrid approach that combines cooperative Binary coded GA (BGA) with Real coded GA (RGA). It employs an adaptive parameter-based hybrid scheme that distributes the computational power and regulates the interactions between the cooperative versions, which operate in a sequential time-interleaving manner. In this study, we aim to extend BRGA to constrained problems by introducing a modified dynamic penalty function into the architecture of BRGA. We use the CEC’2010 benchmark suite of 18 functions to analyze the quality, time and scalability performance of BRGA. To investigate the effectiveness of the proposed modification, we compare the performance of BRGA under both the original and the modified penalty functions. Moreover, to demonstrate the performance of BRGA, we compare it with the performance of some other EAs from the literature. We also implement a robust parameter tuning procedure that relies on techniques from statistical testing, experimental design and Response Surface Methodology (RSM) to estimate the optimal values for the control parameters to secure a good performance by BRGA against specific problems at hand.

Chaotic Artificial Bee Colony Hybrid Discrete Constrained Optimization Algorithm

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. The Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. This paper presents Bee Swarm Optimization intended to introduce chaotic sequences into the algorithm. On the basis of Artificial Bee Colony Optimization Algorithm, a new algorithm by introducing constructing dynamic penalty function and chaotic sequences was presented. Based on Matlab software, the program CABC1.0 with hybrid discrete variables for the proposed algorithm was developed. The complex examples are given. The results show that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence.

Improved Genetic Optimization of Electric Motorcycle Planetary Transmission

Determination of dedendum circle diameter of ring gear in planetary gear drives is an important issue in preliminary design of electric motorcycle transmission. Genetic optimization is used to automate preliminary design of gears by minimizing volume of gear trains. An improved genetic optimization method was applied to a planetary gear transmission of electric motorcycle. Gray code way was applied in variable binary strings for improve research efficiency. Dynamic penalty functions were introduced to the objective function for handing the design constraints. The results were compared with a enumeration method usually applied. Improved GA produced quite well results promptly supplying design parameters of a planetary gear transmission.

An Optimization Design Method for Differential-Based Coupling Power-Slit Device

For a typical Full-hybrid electric vehicle (HEV) layout, such as Toyota Prius, the power-slit device (PSD) is a key component. Determination of volume or weight of a PSD is an important issue in preliminary design of power transmission applications. The purpose of this paper is to preliminary design of PSD gear drives by minimizing volume of gear trains. A stochastic approach Genetic Algorithm (GA) was applied to a differential-based coupling PSD design problem. Static and dynamic penalty functions were introduced to the objective function for handling the design constraints. The calculation of quadratic mean deviation and dynamic bounding parameter values such as determination of crossover and mutation parameter values were adopted to ensure the convergence of the fitness function. The optimized design method based on GA produced quite well results, which promptly supplied preliminary design parameters of gear drives for differential-based coupling PSD design.

Differential Evolution Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

The optimization design about hybrid discrete variables is very significant but also difficult in engineering, mathematics for programming and operational research. Aimed at shortages of existing optimum methods, in this paper, according to the search mechanism of differential evolution algorithm (DEA), a new differential evolution algorithm is proposed to complex optimization problem with hybrid discrete variables The dynamic penalty function was constructed. DEA algorithm program with hybrid discrete variables is developed. The computing examples of mechanical optimization design show that this algorithm has no special requirements on the characteristics of optimal designing problems, it has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence and high efficiency.

Chaotic Artificial Bee Colony Constrained Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization

Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. This paper presents Bee Swarm Optimization intended to introduce chaotic sequences into the algorithm. On the basis of Artificial Bee Colony Optimization Algorithm, a new algorithm by introducing constructing dynamic penalty function and chaotic sequences was presented. Based on Matlab software, the program CABCOA1.0 with hybrid discrete variables for the proposed algorithm was developed. The complex mechanical optimization was given. The results show that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence.

Improved Differential Evolution Algorithm for Structural Optimization Design of Flumes with Hybrid Discrete Variables

The traditional method applying to solve continuous variable optimization problems is not suit for flume structural optimization design with hybrid discrete variable. According to the mathematical model of structural optimum design of the prestressed U-shell flumes, differential evolution (DE) algorithm was introduced to flume structural optimization design. In order to improve the population’s diversity and the ability of escaping from the local optimum, a self-adapting crossover probability factor was presented. Furthermore, a chaotic sequence based on logistic map was employed to self-adaptively adjust mutation factor based on linear crossover, which can improve the convergence of DE algorithm. Dynamic penalty function, to transform the constrained problem to unconstrained one, was employed. The result shows that, compared with the original design scheme, the optimization design scheme can greatly reduce the amount of prestressed reinforcement. The construction cost of both the flume and the whole project can be reduced accordingly.

Research on Materialized View Selection in the Data Warehouse

Materialized view is an effective method for improving the efficiency of queries in data warehouse system, and the problem of materialized view selection is one of the most important decisions. In this paper, an algorithm was proposed to select a set of materialized views under maintenance cost constraints for the purpose of minimizing the total query processing cost; the algorithm adopts the dynamic penalty function to solve the resource constraints view selection. The experimental study shows that the algorithm has better solutions and high efficiency.

Dynamic penalty function approach for ramp metering with equity constraints

This study proposes a non-linear programming optimization model that could simultaneously consider the ramp metering efficiency and equity issue. Equity indexes are defined to capture the degree of equity of the ramp metering, which are formulated as constraints in the model. Dynamic penalty method is adopted to deal with the constraints since they are soft in nature in which solutions with small violations could be acceptable. A modified cell transmission model (MCTM) is chosen to simulate the traffic flow propagation in the network where the objective function value is derived from it. The results of the case study show that the proposed methodology could give better solutions compared to initial solutions. However, the equity indexes obtained are low. It is anticipated that the results for equity indexes can be improved if the penalty coefficients could be fine tuned properly.

Dynamic Sub-Population Genetic Algorithm Combined with Dynamic Penalty Function to Solve Constrained Optimization Problems

This paper presents a new method of dynamic sub-population genetic algorithm combined with modified dynamic penalty function to solve constrained optimization problems. The new method ensures the final optimal solution yields all constraints through re-organizing all individuals of each generation into two sub-populations according to the feasibility of individuals. And the modified dynamic penalty function gradually increases the punishment to bad individuals with the development of the evolution. With the help of the penalty function and other improvements, the new algorithm prevents local convergence and iteration wandering fluctuations. Typical instances are used to evaluate the optimizing performance of this new method; and the result shows that it can deal with constrained optimization problems well.