Single-objective Genetic Algorithm Research Articles

Model-based control of axisymmetric hexarot parallel manipulators

In this research, the control of a hexarot parallel mechanism is investigated. Parallel kinematic simulators or machine tools manipulators suffer from many problems to have a good motion tracking. The inverse kinematics and dynamic formulation that have been developed recently via the Newton–Euler method for a general hexarot mechanism is briefly presented. PID controller and fuzzy incremental controller have been designed and developed as an alternative option of the model-based controller. The efficiency of the model-based controller compared to other controllers has been evaluated. An important contribution of this paper is an appropriate control strategy that improves the tracking performance utilizing the cost effective joint-space sensor. The control scheme development starts with the robust design of the controller-observer of the single actuators. The single objective genetic algorithm is used to tune the controller gains to achieve the least tracking error of joint position. This, moreover, is improved by a centralized feedforward dynamics compensation of the gravity. The validity of the developed model is checked under Simulink environment of MATLAB software, and it is found that the developed model-based controller has the preferable tracking performance rather than the traditional PID and fuzzy incremental controllers The proposed model-based controller is able to decrease the motion tracking error from 55, 20 and 12 to 4 (deg2) for spiral motion compared with PID, FIC and observer-PD, respectively.

Impact of predictor variables on the performance of future occupancy prediction: Feature selection using genetic algorithms and machine learning

This study analyzes the impact of employing different features on the performance of future occupancy prediction models. The aim is to identify the most effective predictor variables for occupancy models in order to enhance the prediction performance. To this end, a multi-objective genetic algorithm (MOGA) is proposed and employed to maximize accuracy while minimizing the number of features utilized in the model. A trade-off between the mentioned objectives is provided by using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the MOGA is compared with that of a single-objective genetic algorithm, forward sequential selection, and backward sequential selection methodologies to assess the effectiveness of the proposed method. The results reveal that the MOGA provides superior performance and is able to improve the median accuracy by up to 4.81% from 70.94% for long-term occupancy prediction based on backward sequential feature selection while utilizing fewer features. Based on the TOPSIS method, no more than six features are required for developing the occupancy models with recent occupancy states and day of week selected as the essential features. In some cases, using recent CO2 levels, occupancy duration, lighting states, and previous occupancy states also shows a potential improvement in the prediction performance.

Simulated annealing assisted NSGA-III-based multi-objective analog IC sizing tool

Over the last few decades, several CAD tools using random search techniques have been developed to find a set of optimum solutions for analog IC design. Although most of these tools have been successful for one or two objective designs, they tend to become ineffective to fulfill the requirements of the desired Pareto-Optimal Front (POF) for many and multi-objective (MO) problems. To address this issue, we propose a novel multi-objective analog circuit optimization tool that employs a hybrid guided random search technique. We adapted the Non-dominated Sorting Genetic Algorithm 3 (NSGA-III) to the analog circuit sizing problem and combined it with simulated annealing (SA) based single-objective (SO) genetic algorithm (GA). This implementation enables the construction of homogeneous POFs in many-dimensional objective spaces. To our best knowledge, this is the first implementation of the NSGA-III in analog electronic design automation (EDA). For demonstration purposes, we optimized two OTA circuits by using the proposed tool, the standard NSGA-III and SPEA-2 based analog circuit optimizers. According to the synthesis results, the proposed tool and 2-objective NSGA-III outperform 2-objective SPEA-2 in terms of POF quality. Specifically, our proposed tool achieved the best results in the maximum spread metrics, outperforming both 2-objective NSGA-III and SPEA2 in all case studies. Furthermore, the number of total simulations required by our method for 3-objective problems is in the same order of magnitude compared to the state-of-the-art methods for 2-objective problems.

Multi-Objective Optimization Design of Radiation Shadow Shield for Space Nuclear Power With Genetic Algorithm

The radiation shadow shield of the space nuclear power is an important component to protect the electronic device at the dose plane. In this paper, design of the radiation shadow shield is first modeled as a multi-objective optimization problem, and then converted into a single objective optimization problem by setting the other sub-objectives as constraints. An optimization method combining a single objective genetic algorithm with Monte Carlo simulation is developed to solve the optimization problem of shadow shield design. Through optimization, weight of the radiation shadow shield can be greatly reduced (14.7% of the weight of the initial design and 16.4% of the weight of the best individual of the first generation), while radiation dose at the dose plane is lower than the acceptable tolerance. The optimization method developed in this work is an automated optimization strategy by searching for the parameter space, which will not be subject to human preferences. The global optimal solutions can be obtained without the tedium of human manual work.

NSGA-II Based Joint Topology and Routing Optimization of Flying Backhaul Networks

This paper addresses the problem of optimizing the deployment of Flying Backhaul Networks (FBNs). The latter comprise Unmanned Aerial Vehicles (UAVs), which are used as access points to provide coverage to a set of ground nodes deployed in a target area. The optimization problem is addressed by means of a Multi-Objective Optimization Algorithm (MOEA), which calculates Pareto curves of UAV placement, providing different trade-offs between the considered objectives: (1) to minimize the number of UAVs, and (2) to maximize the Packet Delivery Ratio (PDR). The selected MOEA is NSGA-II. An embedded single objective Genetic Algorithm (inner-GA) is used to optimize routing, finding the paths that maximize the PDR. In order to obtain consistent solutions for the PDR taking into account MAC layer contention, the scheme makes use of an existing fixed-point algorithm (FPA). Simulation results were obtained for different scenarios combining average versus maximin PDR objective funtions, two different routing optimization algorithms, as well as single sink versus multiple sink traffic patterns.

A frequency response function-based optimization for metamaterial beams considering both location and mass distributions of local resonators

To date, phononic crystals/metamaterials normally adopt classical periodic configurations, and optimization strategies for them are based primarily on dispersion relations from a repeated unit cell. In this study, a frequency response function (FRF) based optimization scheme is presented for a locally resonant metamaterial beam that considers both the resonant frequencies and distribution locations of the resonators. Three optimization objectives involving (1) broadband, (2) multi-band, and (3) high-attenuation characteristics are exploited as study cases, and a single-objective genetic algorithm is used to determine the optimal solutions for the prescribed bandgap targets. The spectral element method is used as an analytic formulation to determine the metamaterial FRFs, and the finite element method is used to validate the effectiveness of the optimization strategy. The results reveal that these objective bandgap characteristics can be enabled without increasing the resonator mass following the proposed optimization procedure. This shows the potential of adjusting the locations and resonance frequencies of resonators in metamaterial beams beyond the widely accepted periodic structures. The most important finding of this study is that promising bandgap properties can be easily realized with only single-degree-of-freedom resonators instead of designing more complex ones or multi-resonators. This FRF-based optimization method can be considered as a simple but instructive strategy for optimal or inverse designs in metamaterials.

Portfolio formation and optimization with continuous realignment: A suggested method for choosing the best portfolio of stocks using variable length NSGA-II

In this paper, we introduce a method of portfolio formation through vertical and horizontal clustering. The clustering algorithm incorporates sufficiently diversified number of stocks in the portfolio with exposure limits on each stock. On obtaining the near Pareto optimal portfolios by using the proposed variable-length Non-dominated Sorting based Genetic Algorithm (NSGA-II), quarter-wise weights of each portfolio’s constituent stocks are determined through the proposed single objective Genetic Algorithm (GA) based Markowitz model. This enables dynamic realignment of the portfolios and can incorporate the macroeconomic environment of the time. The performance of the portfolios is then compared with a benchmark portfolio. Our results show that returns from each of our portfolios, dynamically realigned each quarter, have been able to beat the benchmark index return over our study’s time period. The performance of the clustering algorithm is validated with 4 well-known clustering algorithms.

Stress Wave Propagation in 2D Functionally Graded Media: Optimization of Materials Distribution

In this paper, the analysis and optimization of the effect of the materials distribution on the behavior of 2D functionally graded media subjected to impacted loading has been investigated. First, it is assumed that there are two cases for distributing the components in the FG material. In the first case, the power law is considered for materials distribution, and in the second case, the volume fractional changes of the components are made by third degree interpolation. Considering the elastodynamic behavior of the FG materials under loading, the general governing equations of the wave propagation are extracted for the case of properties variation in two dimensions and then the equations are solved using the finite difference method. Finally, an optimization has been made using a single objective genetic algorithm. The results show that the materials distribution has a considerable effect of stress wave propagation in FGMs.

Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm

In the present analysis 15 experiments were performed in conjunction with the Box-Behnken architecture matrix based on the machining parameter's effect, like spindle speed, feed rate, and cutting width., A surface roughness mathematically framework was designed using the surface reaction methods of this model to aid a genetic algorithm. Which is used to decide the optimum machining parameters. Response surface methodology has been used in this paper due to certain advantages as compare to other methodology such as it needs fewer experiments to study the effects of all the factors and the optimum combination of all the variables can be revealed. Finally, a genetic algorithm was used to determine the optimum setting of process parameters that maximize the rate of content removal. The best surface roughness response value obtained from single-objective genetic algorithm optimization was 1.19 μm.

Structural Optimization of a Steel Arch Bridge with Genetic Algorithm

Structural optimization has become an important tool for structural designers. It helps the designers to find optimal design solutions with better exploitation of materials subject to various constraints. In this article, to face the critical issue of huge horizontal thrust occurred in the Calatrava bridge over the Grand Canal of Venice, single-objective and multi-objective genetic algorithm (GA) based optimization procedures are set up, and the thickness optimization of the bridge is carried out separately with the proposed design procedures and the optimization module implemented in ANSYS. The results are compared in terms of steel volume and horizontal thrust level. It shows that the GA-based design procedures are effective tools to achieve optimal design due to the global search ability of GA. Further, with these powerful tools, more reasonable thickness distributions of steel plates and tubes of the Calatrava bridge can be obtained, meanwhile, the horizontal thrust can be reduced remarkably, and hence the high cost due to the large horizontal thrust of original design could be reduced.

Real-Time Task Schedulers for a High-Performance Multi-Core System

This paper proposes a multi-objective task scheduling algorithm for high performance real-time computing systems designed by the Multicore processor. Most real-time systems are battery powered and operate many complex mechanisms. In such a system, it is necessary to consider the energy consumption, core/processor utilization and deadlock miss rate to improve performance. In order to achieve high efficiency and low power consumption, a multi-objective real-time task scheduler is proposed considering voltage transaction delay, core utilization, unused cores and static and dynamic connection power. Single Objective Genetic Algorithm (GA) and Cellular GA (CGA) are implemented to compare the results with existing methods. The simulation results show that our approach improves performance relatively. Core utilisation is increases from about 5 to 7%. Moreover, the average power consumption decrease is about 12% compared to the existing proposed planners.

Multiple Objective Genetic Algorithms for Solving Traffic Signal Optimization Issue at a Complex Intersection: A Case Study in Taichung City, Taiwan

Background: In optimal traffic signal timing, some researchers proposed a single objective genetic algorithm to optimize the timing plan at an isolated intersection. However, the genetic algorithm belongs to a natural selection procession. It means that a suggested model might have a local, optimal result instead of global optimization. A few researchers have tried to avoid local optimization values by making many assumptions for the suggested model, these estimations lacked comprehensive theoretical bases in the transportation field. Objective: The objective of this study is to contribute a comprehensive optimization solution, by applying multiple objective genetic algorithms, to minimize the effective green time and cycle length at a complex urban intersection. Methods: First, the fitness function was established by the minimum issues of average control delay and queue length at the complex isolated intersection. Secondly, constraint functions were identified based on a scientific basis to provide a comprehensive hypothetical model. After running the hypothetical model with single and multiple objective genetic algorithms and real traffic flow data, the results were compared between the use of multiple genetic algorithms and the use of a single-objective genetic algorithm, between an existing traffic signal timing plan and a suggested traffic signal timing plan. Then, the traffic simulation model for the complex intersection was generated to validate the effectiveness of the suggested method. Results: After comparison, the suggested model was found to be more efficient than the existing traffic signal timing at the complex intersection. Conclusion: This study demonstrated multiple objective genetic algorithms that overwhelmed the single objective genetic algorithm in optimal traffic signal timing. The multiple objective genetic algorithms could be effectively used to handle traffic optimization at a complex large-scale intersection. Furthermore, a comprehensive solution of applying multiple genetic algorithms to deal with traffic signal optimization has been generated in this research.

Development of a highly compact and robust chemical reaction mechanism for the oxidation of tetrahydrofurans under engine relevant conditions

This work presents a compact and robust chemical reaction mechanism for modeling the combustion of saturated furans including tetrahydrofuran, 2-methyltetrahydrofuran and 2-buthyltetrahydrofuran under engine relevant conditions. A decoupling method is adopted to construct the mechanism. The oxidation reaction for the small species is described by a mature and detailed H2/CO/C1 sub-mechanism, based on which the skeletal sub-mechanisms for tetrahydrofuran, 2-methyltetrahydrofuran and 2-buthyltetrahydrofuran are incorporated via a compact yet robust C2-C3 sub-mechanism. The sub-mechanisms for the three tetrahydrofuranic fuel components are selected from the detailed chemical mechanisms in the literature via a series of species rate of production analysis, sensitivity analysis, isomer lumping and reaction lumping. The Arrhenius pre-exponential A factors for these selected reaction pathways are then optimized via a single objective genetic algorithm. The resulting mechanism is rather compact consisting of only 56 species among 183 reactions. The performance of the developed mechanism for predicting the combustion chemistry for the three fuel components has been evaluated against the experimental measurements in the literature. Reasonable agreement between the predicted ignition delay times, speciation profiles and laminar flame speeds with the experimental data is achieved for all the cases considered, indicating the high accuracy and robustness of the developed mechanism.

Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm

ABSTRACT Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitation and cybersecurity attacks in which it leads business discontinuity or even business failure. Multi-cloud, on the other hand, can be trustable paradigm to obviate obstacles such as aforesaid unpleasant features of a single cloud. One of the biggest challenges is to know which cloud is commensurate with user’s business process with regards to security objectives. To this end, the new method is presented to quantify the amount of cloud security risk (CSR) in regards to user’s business process. Therefore, in this paper, the web service composition problem is formulated to bi-objective optimisation problem with service cost and multi-cloud risk viewpoints in ever-increasing multi-cloud environment (MCE) in which each provider has its variable pricing policy and different security level. It is obviously an NP-Hard problem. To solve the combinatorial problem, we develop a bi-objective time-varying particle swarm optimisation (BOTV-PSO) algorithm. The parameters are tuned based on elapsed time so a good balance between exploration and exploitation is achieved. To illustrate the effectiveness of proposed algorithm, we defined several scenarios and compared the performance of proposed algorithm with multi-objective GA-based (MOGA) optimiser, a single objective genetic algorithm (SOGA) that only optimises cost function and neglects CSR, and multi-objective simulated annealing algorithm (MOSA). The experimental results showed the superiority of proposed BOTV-PSO against other approaches in terms of convergence, diversity, fitness, performance, and even scalability.

Optimization of temperature distribution and heat flux functions for cylindrical and conical micro-fins by applying genetic algorithm

One of the engineering researches is improving the devises performance. In fact, after the devises are designed, optimization is necessary to obtain more performance, less cost and fast turnaround production. In this research, the single- and multi-objective genetic algorithms were applied to optimize the distribution of temperature and heat flux functions for both cylindrical and conical micro-pin fins. First, each equation was optimized separately in single-objective genetic algorithm, and then, both equations were optimized interdependently in multi-objective genetic algorithm. The desired values of derived equations are less temperature and more heat flux within micro-pin fins. The flow conditions, material properties and geometrical parameters were different cases which were simultaneously used in single- and multi-objective optimization approaches. The optimization parameters involved the design of the critical parameters of the micro-pin fins, including the size of the root and tip radius, length of micro-pin fin, fin and fluid thermal conductivity coefficients, convection heat transfer coefficient and relative roughness. Results showed that for cylindrical pin fin, in each arrangement, the value of 0.0 as minimum temperature distribution function was found, which means with suitable pin fin design, the fin tip temperature could reach to the environment temperature. The minimum temperature distribution function for conical pin fin was found to be near zero. Among four considered cases, geometry limitation had the maximum temperature value of 0.0045. The fin root radius and the coolant conductivity had significant effects on the cylindrical and conical pin fin, respectively.

Shape Optimization of Thin-Walled Cross Section for Automobile Body Considering Stamping Cost, Manufacturability and Structural Stiffness

At the conceptual design stage, the cross-sectional shapes of thin-walled beams (TWBs) need to be optimized to improve the performance of automobile structure. Therefore, this paper innovatively proposes a cross-sectional shape optimization method to acquire a low cost, high stiffness, manufacturable and lightweight TWB. Meanwhile, the stamping cost of thin-walled beams is analytically derived. The cross-sectional area is taken as the objective function to establish an optimization formulation with multiple constraints such as cross-sectional stiffness, manufacturability and cost of TWBs. The single-objective genetic algorithm based on non-dominant principle is introduced to solve the problem. The effectiveness of the proposed method is verified by an example of double cell cross-sectional shape.

Orienting Conflicted Graph Edges Using Genetic Algorithms to Discover Pathways in Protein-Protein Interaction Networks.

Advanced computational techniques of the current era help to identify proteins from the complex biological network that interact with each other and with the cell's environment. Biological pathways are a chain of molecular actions that leads to a new molecular product creation or alters the cellular state. These pathways are helpful in the predication of many real-world issues. Rebuilding these pathways is a challenging task due to the fact that protein interactions are undirected, whereas pathways are directed. To discover these pathways in protein-protein interaction data from specified source and target, it is essential to orient protein interactions. Unfortunately, the edge orientation problem is NP-hard, which makes it challenging to develop effective algorithms. This work rebuilds biologically important pathways in a weighted network of protein interactions of yeast species. The proposed algorithm, pseudo-guided multi-objective genetic algorithm (PGMOGA) rebuilds pathways by assigning orientation to the edges of the weighted network. Extending the past research, mathematical modeling of single-objective and multi-objective functions is performed. The PGMOGA is compared with four state-of-the-art approaches, namely, random orientation plus local search (ROLS), single-objective genetic algorithm (SOGA), multi-objective genetic algorithm (MOGA), and multi random search (MRS). The comparison is based on three general and four path specific metrics. Results show that the current proposal performs better.

Evolutionary multiobjective optimization to target social network influentials in viral marketing

Marketers have an important asset if they effectively target social networks’ influentials. They can advertise products or services with free items or discounts to spread positive opinions to other consumers (i.e., word-of-mouth). However, main research on choosing the best influentials to target is single-objective and mainly focused on maximizing sales revenue. In this paper we propose a multiobjective approach to the influence maximization problem with the aim of increasing the revenue of viral marketing campaigns while reducing the costs. By using local social network metrics to locate influentials, we apply two evolutionary multiobjective optimization algorithms, NSGA-II and MOEA/D, a multiobjective adaptation of a single-objective genetic algorithm, and a greedy algorithm. Our proposal uses a realistic agent-based market framework to evaluate the fitness of the chromosomes by simulating the viral campaigns. The framework also generates, in a single run, a set of non-dominated solutions that allows marketers to consider multiple targeting options . The algorithms are evaluated on five network topologies and a real data-generated social network, showing that both MOEA/D and NSGA-II outperform the single-objective and the greedy approaches. More interestingly, we show a clear correlation between the algorithms’ performance and the diffusion features of the social networks.

Analysis of eddy current field for new type of thrust bearings based on single-objective genetic algorithm

Based on the working condition of the magnetic thrust bearing (MTB), the single-objective optimization model (SOOM) is built. The optimum design of structure parameters for two types of magnetic bearings including traditional carbon bearing and soft magnetic composite (SMCs) bearing has been carried out using genetic algorithm. The analysis of eddy current field for two types of bearings are obtained by FEM. It is found that the maximum eddy current field of SMCs bearing is less than that of carbon steel one, and the air gap magnetic density is larger than that of carbon steel bearing at the same frequency. SMCs obtained by the compression of insulated soft magnetic powders have low eddy current. Thus, under the same magnetic force conditions, the input current of SMCs bearings is lower than that of carbon steel ones. The analysis shows that SMCs can replace carbon steel and has superiority in bearing application.

Method of the Mission Planning for the Communication between the Small Satellite Clusters

Mission planning of space astronomical satellite is a complex optimization problem, which is to determine the communication activities needed by space astronomical and research the in-orbit plan. By abstracting the relevant elements of the mission planning problems of space astronomical satellite and establishing the mathematical model of mission planning of the space astronomical satellite, we introduce the Genetic Algorithm and design the single-objective Genetic Algorithm based on the communication mission window. In addition, based on the Genetic Algorithm, a multi-objective Genetic Algorithm based on the sequence of communication window is designed, which improves the coding ability of Genetic Algorithm and improves the flexibility and applicability of planning effect. From the results of planning simulation, this paper not only innovatively introduces Genetic Algorithm into mission planning of satellite and ground data in order to improve the efficiency of mission planning of space astronomical satellite, but also optimizes single-objective mission to multi-objective mission, which improves the applicability of mission planning of satellite communication and provides reference for other relevant researches in the future.