Search Space Of Genetic Algorithm Research Articles

Research on procedure optimisation for composite grinding based on Digital Twin technology

The complexity of composite grinding movement, the variety of machining features and available grinding wheels, and the changing working conditions pose a challenge to the rapid programming of safe and efficient composite grinding procedure. The procedure optimisation plays an important role in solving this difficult problem. Therefore, a procedure optimisation method is proposed for composite grinding based on a Digital Twin (DT) system, which takes procedure time as optimisation objective to achieve high efficiency and ensures process rationality and safety by constructing corresponding constraint conditions. Moreover, the actual working conditions mapped into the DT system, such as workpiece parameters, machining requirements, grinding wheel parameters and status, machine tool motion position, and so on, are obtained to update the parameters involved in the optimisation model. And thus, the proposed method has the ability to timely find the optimal procedure under changing working conditions. In addition, a combination algorithm based on genetic algorithm (GA) and dynamic programming is proposed, which greatly reduces the search space of GA and realises the two-class co-optimisation of grinding wheel selection and procedure. Finally, the case study verifies the effectiveness of the proposed method to reduce procedure time and the dynamic response ability to changing working conditions.

An environmental based techno-economic assessment for battery energy storage system allocation in distribution system using new node voltage deviation sensitivity approach

Photovoltaic project with Battery energy storage system (BESS) provide a sustainable and low-cost fuel technology to world and would help to meet world countries decarbonization target. This paper presents a multi-objective based methodology for BESS allocation in distribution networks. In proposed work, objective function combines various benefits from energy arbitrage, energy losses reduction, reduction in transmission access fee and environmental emission and reduction in investment costs of BESS. The new node voltage sensitivity analysis strategy utilized in initial step to recognize and visualize the prominent nodes in system and additionally decreases the search space for genetic algorithm (GA). Battery charge/discharge function is formulated for optimal allocation and every day charge/discharge of BESS. The proposed method is tested on IEEE 33- bus network. The results of this method shows reduction in energy losses, environmental emission and improvement in system parameter are also observed.

Maximum likelihood and maximum product of spacings estimations for the parameters of skew-normal distribution under doubly type II censoring using genetic algorithm

In this study, we use the maximum likelihood (ML) and the maximum product of spacings (MPS) methodologies to estimate the location, scale and skewness parameters of the skew-normal distribution under doubly type II censoring. However, it is known that these estimators cannot be obtained analytically because of nonlinear functions in the estimating equations. Therefore, numerical methods such as Nelder–Mead (NM), Newton–Raphson (NR), iteratively re-weighting algorithm (IRA), etc., are used to overcome this problem. In this study, different than the earlier studies, we use the genetic algorithm (GA) which is a population-based heuristic method based on the random search to find the estimates of the unknown parameters. In constructing the search space for GA, we utilize the robust confidence intervals to have a high convergence rate in GA. Then, we compare the efficiencies of the ML estimators obtained by using NM, NR, IRA, and GA methods and the efficiencies of the MPS estimators obtained by using NM and GA methods via an extensive Monte-Carlo simulation study. At the end of the study, we analyze the deep-groove ball bearings data to show the implementation of the proposed methodology.

A Genetic Algorithm Based and Subtree Restricted Routing Algorithm in Wireless Sensor Networks

The GA(genetic algorithms) was applied to find routing trees with minimal maximal sensor load thus extend network lifetime in wireless sensor networks. However, the existing algorithmlimits search space of GA to avoid generating cycles by dividing sensors into layers and stipulating that a sensor only can select a sensor in its lower layer as its new parent. In this paper, we have found that how cycles are generated during GA operations, and proposed a solution based on subtree to avoid generating cycles during GA operations.The solution only requires a sensor not to change its parent to a sensor in the subtree with it as the root. And layers are no longer considered. As a result, GA has more search space and GA operations can be performed more freely. Thus more optimal routing trees are obtained and network lifetime is extended. The experiment shows that our proposed algorithm extend network lifetime notably.

A Hybrid Method for Optimal Siting and Sizing of Battery Energy Storage Systems in Unbalanced Low Voltage Microgrids

This paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues and whose constraints involve technical limitations of both network and distributed resources. Emphasis is given to the power quality issue with particular attention to unbalance reduction and voltage profile improvement. Technological issues, such as those related to the preservation of batteries’ lifetime, were also taken into account. The planning problem is solved by means of a genetic algorithm which includes an inner algorithm based on sequential quadratic programming. In order to limit the processing time while maintaining reasonable accuracy, the genetic algorithm search space is significantly reduced identifying a subset of candidate buses for the siting of the storage resources. The Inherent Structure Theory of Networks and the Loading Constraints Criterion were used to identify the candidate buses. The proposed method has been applied to a low voltage test network demonstrating the effectiveness of the procedure in terms of computational burden while also preserving the accuracy of the solution.

Application independent localisation of vehicle plate number using multi‐window‐size binarisation and semi‐hybrid genetic algorithm

This study introduces an enhanced version of a previously published genetic algorithm based technique to allow fast and accurate detection of the vehicle plate number independently of the used application. Hence, significant enhancements are introduced to upgrade the genetic algorithm into a semi-hybrid category by preceding it with a sorting and subgrouping process that reduces the search space and following it by a rule-based local search to optimize its output. The updated population structure and its initiation with subsets of the sorted image foreground objects results in a linear relationship between the image complexity represented by the number of foreground objects and the genetic algorithm search space instead of being exponential in previous versions. Moreover, a novel extra variable-size-window adaptive binarisation step is introduced to overcome the problems of attached license symbols that cannot be solved by the skipping ability introduced in previous versions. Various image samples with a wide range in scale, orientation, and symbol connectivity have been experimented to verify the effects of the new improvements. Encouraging results with 99.2% detection accuracy have been reported using a public dataset outperforming the author of the dataset by more than 5.5% and the state of the art systems by 2%.

Vibration-based damage detection in beams by genetic algorithm encoding locations and damage factors as decision variables

Vibration-based damage detection is based on the fact that vibration characteristics such as natural frequencies and mode shapes of structures are changed when the damage is happened. The vibration-based damage detection of a beam is formulated as a single-objective optimization problem in which genetic algorithm (GA) is used as the optimizer. This paper presents the encoding by locations and damage factor (ELD) which employs location and damage amount as the decision variables. The proposed encoding can reduce the number of decision variables that used in the previous encoding, the encoding by damage factor of each element (EDE). The search space of GA with ELD is then smaller than that of GA with EDE. The simulation results reveal that GA with ELD can identify the damage occurred in the beam more correctly than GA with EDE. Moreover, the damage predicted by GA with ELD is quite close to the actual damage for all 3 test cases.

Minimizing economic and environmental impacts through an optimal preventive replacement schedule: Model and application

This paper presents a mathematical model to determine the optimal schedule of preventive replacement of a component such that the economic and environmental impacts of the component are minimized. For the economic dimension, the model minimizes the operation, failure and replacement costs of the component. From the environmental perspective, the model aims to minimize the environmental impact associated with the use phase and action taken to replace the component. The model is made general and can accommodate any environmental impact category. Due to the complexity of the objective functions of the model, genetic algorithm (GA) is proposed to find the optimal solutions. To reduce GA search space, upper and lower bounds of the solutions are determined based on the numerical analysis of the first derivatives of the objective functions of the model. To show the applicability of the model, a case study aiming to minimize total expected cost and global warming potential (GWP) of a bus tire is presented. The results of the case study show that the optimal preventive replacement schedule minimizing total expected cost per km is when the tire reaches 17,700 km and the schedule minimizing total expected GWP per km is when the tire reaches 19,500 km. The schedules result in US$23 and 0.2 kg CO2-eq savings in the total expected cost and GWP per tire. The solutions of the multi-objective optimization problem indicate that a 1000 km increase in the optimal schedule minimizing total cost will result in a 0.4% increase in the total expected cost and a 0.002% reduction in the total expected GWP of the tire. The sensitivity analysis presents that 1% reduction in the operation cost and fuel consumption contributes to a 0.91% reduction in the total expected cost and 0.99% reduction in the total expected GWP, respectively.

GenFin: Genetic Algorithm-Based Multiobjective Statistical Logic Circuit Optimization Using Incremental Statistical Analysis

As the semiconductor technology node scales into the deep submicrometer regime, it has become very difficult to obtain high IC yields because the process–voltage–temperature variations induce large spreads in delay and power. In this paper, we propose a new framework, called GenFin, which is, as far as we know, the first to target the multiobjective yield optimization of logic circuits. Since FinFETs are a promising substitute for CMOS at 22-nm technology node and beyond, we evaluate the framework with a 22-nm FinFET logic library. By combining the power of genetic algorithm (GA) and adaptive multiobjective optimization, GenFin produces a set of nondominated logic circuits whose timing, leakage power, and dynamic power yields are simultaneously optimized. This can help designers make tradeoff decisions wisely and avoid suboptimal solutions. We also propose an incremental statistical circuit analyzer, called incremental FinPrin, that speeds up the statistical static timing analysis by up to $9.6\times $ and the statistical power analysis by up to $2235.7\times $ , while incurring errors of only up to 0.031% in mean and 0.74% in standard deviation relative to nonincremental analysis. We use heuristics based on the deterministic timing analysis and gate criticality to reduce the GA search space and also improve the quality of its solutions. We present extensive experimental results to demonstrate the efficacy of GenFin.

Linkage-Based Distance Metric in the Search Space of Genetic Algorithms

We propose a new distance metric, based on the linkage of genes, in the search space of genetic algorithms. This second-order distance measure is derived from the gene interaction graph and first-order distance, which is a natural distance in chromosomal spaces. We show that the proposed measure forms a metric space and can be computed efficiently. As an example application, we demonstrate how this measure can be used to estimate the extent to which gene rearrangement improves the performance of genetic algorithms.

PCB Assembly Process Optimization based on Constraint Model

The structure and assembly process of Chip Shooter (CS) machine are studied. The factors of engineering practice such as the component size and machine structure etc. are considered. The assembly optimization model of Printed Circuit Board (PCB) on CS machine is formulated. A hybrid genetic algorithm (HGA) which combine with Polychromatic Sets (PS) and Genetic Algorithm (GA) is presented. A binary piece-wise coding method is proposed. In the individual chromosome, feeder arrangement and component placement sequence is described simultaneously. Constraint model is used to consider the impact of component size on the assembly speed and the feeder arrangement of CS machine. Meanwhile the constraint model improved the quality of initial population, ensured the effectiveness of GA operation and narrowed the search space of GA obviously. Calculation example indicated that HGA optimize the component sequence and feeder arrangement simultaneously and achieve excellent practicability. The assembly efficiency of CS machine is improved obviously.

Genetic algorithm search space splicing particle swarm optimization as general-purpose optimizer

A heuristic search space splicing scheme has been implemented to aid the convergence of the particle swarm optimization (PSO) algorithm to the global optimum. Genetic algorithm (GA) was used to splice the search space into smaller subspaces, thereby reducing the number of local minima. PSO algorithm was subsequently used to locate the global optima in the subspaces. A set of 11 well-known test functions had been used for the assessment of this novel GA search space splicing PSO (GA-SSS-PSO) architecture. Of the methods tested in this study, the GA-SSS-PSO approach was the only one that could optimize all functions to a desirable level. To demonstrate the algorithm's applicability, three optimization tasks of different categories commonly faced in the field of chemometrics were subjected to optimization by GA-SSS-PSO and results indicated that the novel hybrid algorithm provided robust performance for both theoretical and real life problems and may be suited as general-purpose optimizer for medium-sized optimization tasks.

QoS Routing in Ad-Hoc Networks Using GA and Multi-Objective Optimization

Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).

Optimal allocation of capacitors in unbalanced multi-converter distribution systems: A comparison of some fast techniques based on genetic algorithms

Genetic algorithms (GAs) are widely used for optimal allocation of capacitors in distribution systems. When dealing with large-scale systems, such as in case of unbalanced multi-converter distribution systems, these algorithms can require significant computational efforts, which reduce their effectiveness. In order to reduce processing time for GAs and simultaneously maintain adequate levels of accuracy, methods based on the reduction of the search space of GAs or based on micro-genetic algorithms have been proposed. These methods generally guarantee good solutions with acceptable levels of computational effort. In this paper, some fast, GA-based methods are compared and applied for solving the problem of optimal sizing and siting of capacitors in unbalanced multi-converter distribution systems. The algorithms have been implemented and tested on the unbalanced IEEE 34-bus test distribution system, and their performances have been compared with the performance of the simple genetic algorithm technique.