Mixed Genetic Algorithm Research Articles

Multi-objective shape optimization of plate structure under stress criteria based on sub-structured mixed FEM and genetic algorithms

This paper presents a methodology for the multi-objective (MO) shape optimization of plate structure under stress criteria, based on a mixed Finite Element Model (FEM) enhanced with a sub-structuring method. The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions and considers thickness distributions parameters and antagonist objectives among them stress criteria. We implement a displacement-stress Dynamic Mixed FEM (DM-FEM) for plate structure vibrations analysis. Such a model gives a privileged access to the stress within the plate structure compared to primal classical FEM, and features a linear dependence to the thickness parameters. A sub-structuring reduction method is also computed in order to reduce the size of the mixed FEM and split the given structure into smaller ones with their own thickness parameters. Those methods combined enable a fast and stress-wise efficient structure analysis, and improve the performance of the repetitive GA. A few cases of minimizing the mass and the maximum Von Mises stress within a plate structure under a dynamic load put forward the relevance of our method with promising results. It is able to satisfy multiple damage criteria with different thickness distributions, and use a smaller FEM.

Study on Combinational Scheduling Optimization of Bus Transit Rapid Based on Tabu Search & Genetic Algorithm

The goal is to minimize the sum of operating cost and passengers’ travel cost, and establish an optimized combinational scheduling model of Bus Rapid Transit (BRT) combined with regular bus, express bus and shuttle bus. A mixed genetic algorithm based on tabu search algorithm (GA-TS) has been designed after analyzing the fundamental principle of genetic algorithm (GA) and tabu search (TS). A case study has been carried out on the combinational scheduling optimization of a selecting BRT line. By adopting the combinational scheduling model, 5.24% of the total system cost can be saved, which is quite prominent. The mixed genetic algorithm based on GA-TS can optimize the BRT scheduling system, shorten the turnaround time of operating BRT vehicles, effectively reduce the total system cost of BRT and improve decision-making efficiency and service quality.

Optimization of Postal Distribution Network Based on Rendezvous with Heterogeneous Vehicles and Capacity Constraints

High unloaded ratio of trucks is one of the key factors that incur high costs of mail transportation in China's postal network. How to reduce unnecessary deadhead kilometers of trucks is worthy of being researched. In this paper, the authors take heterogeneous trucks and facility capacity into consideration of optimizing the postal express line network using Rendezvous driving pattern of trucks. A mixed integer non-linear programming model is built and a corresponding mixed genetic algorithm is developed to solve the model. A numerical simulation is also designed to verify the validity of the model and the algorithm developed in this research. Through a comparison of Rendezvous and direct travel mode, some insights of Rendezvous are presented.

S0830301 昭和基地マイクログリッドのエンジン発電機の最適運用計画に関する検討

The ship Shirase in charge of transporting fuel and research equipment to the Japanese Showa Base in the Antarctica is approaching its loading capacity limit due to the increasing research activities and new requirements for ambient comfort in the base. Also, due to the negative effects of the emissions caused by the burning of fossil fuels, the increasing utilization of renewable energy and the reduction of fuel consumption represent important challenges. In order to reduce fuel consumption and to increase the utilization of renewable energy, we are considering the implementation of a microgrid for the base named the Showa Base Microgrid (SBMG). In this work, the microgrid is considered to be composed, together with the solar and wind generators, of distributed engine generators, a seawater source heat pump and electric thermal storage heaters, and an operation plan is implemented. In this work, as analysis methods, we propose a mixed integer linear programming (Analysis A) and genetic algorithm (GA) (Analysis B). For these two methods, the amounts of fuel consumption are calculated, and the difference between the amounts of fuel consumption for each method, together with the differences in operation for the engine generators are evaluated.

Effective Hamiltonian for two interacting double-dot exchange-only qubits and their controlled-NOT operations

Double-dot exchange-only qubit represents a promising compromise between high speed and simple fabrication in solid-state implementations. A couple of interacting double-dot exchange-only qubits, each composed by three electrons distributed in a double quantum dot, is exploited to realize controlled-NOT (CNOT) operations. The effective Hamiltonian model of the composite system is expressed by only exchange interactions between pairs of spins. Consequently, the evolution operator has a simple form and represents the starting point for the research of sequences of operations that realize CNOT gates. Two different geometrical configurations of the pair are considered, and a numerical mixed simplex and genetic algorithm is used. We compare the nonphysical case in which all the interactions are controllable from the external and the realistic condition in which intra-dot interactions are fixed by the geometry of the system. In the latter case, we find the CNOT sequences for both the geometrical configurations and we considered a qubit system where electrons are electrostatically confined in two quantum dots in a silicon nanowire. The effects of the geometrical sizes of the nanowire and of the gates on the fundamental parameters controlling the qubit are studied by exploiting a spin-density-functional theory-based simulator. Consequently, CNOT gate performances are evaluated.

A mixed discrete-continuous variable multiobjective genetic algorithm for targeted implementation of nonpoint source pollution control practices

[1] Implementation of nonpoint source (NPS) pollution control strategies at the watershed scale hinges on abating pollutant movement from the landscape to water bodies at minimum cost. This paper presents an integrated simulation-optimization approach for targeted implementation of agricultural conservation practices at the watershed scale. A multiobjective genetic algorithm (NSGA-II) with mixed discrete-continuous decision variables was coupled with a distributed watershed model, Soil and Water Assessment Tool (SWAT), to identify optimal types and locations of conservation practices for nutrient and pesticide control at the watershed scale. Previous optimization studies have used binary representation of nonpoint source pollution controls, even though many could be better characterized as continuous variables. In this study, a novel discrete-continuous decision variable, also known as mixed-variable, representation was used to enhance the versatility of the approach by evaluating more options during the search process. Application of the proposed framework in the Eagle Creek Watershed, Indiana, indicated that the optimal suite of conservation practices from the mixed-variable NSGA-II was more effective in meeting water quality targets at lower costs than the solution from binary-variable optimization. However, the mixed-variable approach was considerably more computationally demanding for assessing tradeoffs between environmental and economic factors. A method for hybridization of binary and mixed-variable NSGA-II methods in the context of nonpoint source pollution control practices was developed to enhance the computational efficiency of the optimization procedure. As a result, the number of model simulations required for convergence to the Pareto-optimal solutions was reduced by 96%. The conceptual complexity and computational requirements of optimization-based approaches are impediments to their wider application for targeted implementation of NPS pollution control strategies. The methods and finding of this study address these issues and could result in a more effective implementation of management strategies at the watershed scale.

Hybridization of Biogeography-Based

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

Solution of multi-objective optimal power flow using gravitational search algorithm

This article presents application of an efficient and reliable heuristic technique inspired by swarm behaviours in nature namely, gravitational search algorithm (GSA) for solution of multi-objective optimal power flow (OPF) problems. GSA is based on the Newton's law of gravity and mass interactions. In the proposed algorithm, the searcher agents are a collection of masses that interact with each other using laws of gravity and motion of Newton. In order to investigate the performance of the proposed scheme, multi-objective OPF problems are solved. A standard 26-bus and IEEE 118-bus systems with three different individual objectives, namely fuel cost minimisation, active power loss minimisation and voltage deviation minimisation, are considered. In multi-objective problem formulation fuel cost and loss; fuel cost and voltage deviation; fuel cost, loss and voltage deviation are minimised simultaneously. Results obtained by GSA are compared with mixed integer particle swarm optimisation, evolutionary programming, genetic algorithm and biogeography-based optimisation. The results show that the new GSA algorithm outperforms the other techniques in terms of convergence speed and global search ability.

Optimization of the Thermosetting Pultrusion Process by Using Hybrid and Mixed Integer Genetic Algorithms

In this paper thermo-chemical simulation of the pultrusion process of a composite rod is first used as a validation case to ensure that the utilized numerical scheme is stable and converges to results given in literature. Following this validation case, a cylindrical die block with heaters is added to the pultrusion domain of a composite part and thermal contact resistance (TCR) regions at the die-part interface are defined. Two optimization case studies are performed on this new configuration. In the first one, optimal die radius and TCR values are found by using a hybrid genetic algorithm based on a sequential combination of a genetic algorithm (GA) and a local search technique to fit the centerline temperature of the composite with the one calculated in the validation case. In the second optimization study, the productivity of the process is improved by using a mixed integer genetic algorithm (MIGA) such that the total number of heaters is minimized while satisfying the constraints for the maximum composite temperature, the mean of the cure degree at the die exit and the pulling speed.

An Optimization Method of the Heavy Machine Cutting Parameters

This paper analyzes the machining process of CNC machine tools, and builds an optimization model of the machining process parameters based on the mechanical vibration and the operational research. The model mixed genetic algorithm and particle swarm optimization (PSO) is built. It proposes an optimization algorithm that has higher convergence precision and execute ability to solve engineering problem with nonlinear and multi-extremum. According to case study, it proves the correctness of the model and the efficiency and high-performance nature of the designed optimization algorithm. It also appears the efficiency to solve the common engineering problems by the intelligent optimization algorithms.

Complex Genetic Algorithm for Structure Shape Optimization Design of Mixed Discrete Variables

Discrete complex method is used in genetic algorithm(GA) and a mixed genetic algorithm called complex genetic algorithm(CGA) is formed. The complex method used here increases the quality of species groups, and improves the searching efficiency. The mixed genetic algorithm method is used in the shape optimization for mixed discrete variables. The integration and coding of the shape variables and the cross-section variables in genetic algorithm can not only solve the coupling problem of two kinds of variables, but also avoid the partial optimum solution resulting from the separation of the two kinds of variables. The result of the exemplification indicates that the complex genetic algorithm for structure shape optimization design of mixed discrete variables is effective.

Research on Internal Layout Optimization of Logistics Node under the Conditions of Complex Terrain Based on Computer Vision and Geographical Simulation System

This paper solves the problem of logistics node space relationship beyond expression based on computer vision technology, proposes internal layout optimization mathematical model of logistics node on the basis of overall consideration of function zone geometry shape, the optimal area utilization rate, and the minimum material handling cost, and then designs a highly mixed genetic simulated annealing algorithm based on multiagent to get layout solution. Through contrasting, the result has shown that the model and algorithms put forward in this paper can realize large‐scale internal layout optimization of logistics node under the conditions of complex terrain and multiple constraints.

Predicting VOC Concentration Measurements: Cognitive Approach for Sensor Networks

Volatile organic compounds (VOCs) belong to special pollutants included in Kyoto protocol and in its updated versions. They are responsible for great and dangerous air pollution. The volatility of VOCs make them difficult to be computed and to be measured by means of appropriate sensors in terms of accuracy. Nowadays different methods have been adopted and approved by specific authorities. One of the most important is EPA25 issued by the American Environmental Protection Agency. As a matter of fact, EPA25 only works on set of complete data. In case of noncomplete set of data, we mean missing data issue due to different troubles, namely dysfunction concerning networks of sensors, thermal drifts, etc., EPA25 as well as other methods are not able to overcome the above issue that affects the prediction and the reliability. This research proposes an alternative and effective way, based on cognitive approach, to process VOC data delivered by a network of sensors by using a mixed genetic algorithm with an additional fuzzy-based procedure. A comparison with other techniques based on neural networks is also envisaged. The proposed modified genetic algorithm offers the best accuracy.

Fusegates selection and operation: simulation–optimization approach

Fusegates present a reliable and cost-effective alternative, which increase flood protection and water supply benefits. This article develops a comprehensive simulation–optimization framework for practical selection, installation, and operation of fusegates. The computational model simulates the complicated hydraulic behavior of fusegates systems with varying design characteristics and consequential anomalous routing process in case of flood events. An efficient mixed genetic algorithm (GA) is subsequently developed and coupled with the highly nonlinear hydraulic simulation model to minimize the overall expected annual cost under structural, hydraulic, and operational constraints. Types, heights, and tipping heads of gates are explicitly treated as optimization decision variables. Furthermore, the frequent practice of installing all gates in the same level is practically improved to favorably help minimize water loss in case of moderate discharge floods. The proposed model is demonstrated and discussed for a case study of the Taleghan Dam fusegates installation project in Iran. A series of sensitivity analyses are also conducted to assess routing effect and uncertainty in water unit price and replacement costs and provide more insight and understanding of the design problem.

A novel progressively swarmed mixed integer genetic algorithm for security constrained optimal power flow (SCOPF)

This paper proposes a superior Mixed Integer based hybrid Genetic Algorithm (MIGA) which inherits the advantages of binary and real coded Genetic Algorithm approach. The proposed algorithm is applied for the conventional generation cost minimization Optimal Power Flow (OPF) problem and for the Security Constrained Optimal Power Flow problem. Here, the main shortcoming with the conventional Genetic Algorithm, the ‘Hamming Cliff’ problem is addressed with Mixed Genetic Algorithm, which can overcome issues connected to the continuous search space. The proposed algorithm models the continuous variables using real values and discrete variables using binary values. A novel concept of Progressive filling is also presented here for Mixed Integer GA, which heightens the algorithm. The proposed procedure is compared with many conventional algorithms and validated on a test-bed of standard IEEE 30 bus system with and without valve-point loading effect.

Naive Bayesian Classifier Based on Genetic Simulated Annealing Algorithm

Naive Bayesian classifier (NBC) is a simple and effective classifier, but in the actual application, its attribute independence assumption is not always set up. This factor affects its classification performance. Attribute reduction is an effective way to improve the performance of this classification. This paper take advantage of mixed Simulated Annealing and Genetic Algorithms to optimize attribute set, so that a better NBC is constructed. Comparing the based on genetic algorithm NBC with the traditional NBC, experiment results show that the based on genetic algorithm NBC can be more effective and rapid to solve the classification performance of NBC.

Mixed Optimal Algorithm of Resource Allocation in Energy Industry

Abstract Optimized configuration of energy industry is an issue for resource combination and optimization, which includes continuous variable and discrete variable and belongs to the optimal power flow research. In order to improve the solution quality and the counting efficiency, this paper gives the solution using the mixed Greedy Algorithm and Genetic Algorithm: Use the superiority of Greedy Algorithm to obtain the original resource distribution solution space in the structure. This algorithm can minimize the spatial scale. And then utilizes the Genetic Algorithm to select the original solution space based on the overall situation and improve its application efficiency. The test case matches the anticipation.

A conceptual framework for modelling reverse logistics networks

Reverse logistics is receiving more attention because of the growing environmental and economical concerns. Some complex issues depending on social, technical and legislative factors are: how to prevent the environmental deterioration caused by the generation of wastes, how to minimise the generation of wastes, and how to enhance the value recovery from the wastes. In this paper, we have done an exhaustive literature review, highlighting the applications of various modelling approaches from reverse logistics perspectives. The considered modelling approaches are linear programming, mixed integer linear programming, goal programming and genetic algorithm. The reverse logistics issues are basically categorised into five categories namely distribution, production planning and control, information technology, business economics and integration/coordination. The paper proposes a framework focusing these issues and suggests an appropriate approach to model reverse logistics networks.

Optimal Design of Gear Transmission System of Increasing Speed for Half-Direct-Drive Tidal Current Energy Station

The half-direct-drive transmission is conducted for the transmission system of tidal current power stations using a small speed-up ratio of planetary gearbox between turbine and generator. A design model is developed for the optimization of the planetary gear transmission system. And a mixed genetic algorithm is applied on the basis of fundamentals of genetic algorithm to carry out the optimization. From the example calculation, a better design scheme is obtained by the optimization.

Shape, sizing optimization and material selection based on mixed variables and genetic algorithm

In this work, we explore simultaneous designs of materials selection and structural optimization. As the material selection turns out to be a discrete process that finds the optimal distribution of materials over the design domain, it cannot be performed with common gradient-based optimization methods. In this paper, material selection is considered together with the shape and sizing optimization in a framework of multiobjective optimization of tracking the Pareto curve. The idea of mixed variables is often introduced in the case of mono-objective optimization. However, in the case of multi-objective optimization, we still face some hard key points related to the convexity and the continuity of the Pareto domain, which underline the originality of this work. In addition to the above aspect, there is a lack in the literature concerning the industrial applications that consider the mixed parameters. Continuous variables refer to structural parameters such as thickness, diameter and spring elastic constants while material ID is defined as binary design variable for each material. Both mechanical and thermal loads are considered in this work with the aim of minimizing the maximum stress and structural weight simultaneously. The efficiency of the design procedure is demonstrated through various numerical examples.