Crossover Of Genetic Algorithm Research Articles

Effective data placement for scientific workflows in mobile edge computing using genetic particle swarm optimization

SummaryMobile edge computing (MEC) necessitates cost‐effective deployment for executing scientific workflows with different tasks and datasets, which provides computing, storage and network control at the network edge. However, the execution of scientific workflows in MEC results in heavy costs of data placement including data transmission and data storage. Although there are solutions for data placement in traditional cloud computing, they cannot effectively respond to the latency‐sensitive property of scientific workflows, which leads to the excessive costs of data placement. To cope with this problem, we combine the advantages of MEC and cloud computing and propose a genetic algorithm particle swarm optimization (GAPSO) based method to explore the optimal strategy of data placement for scientific workflows in MEC. First, a unified model of data placement is designed to explore a cost‐effective strategy, which considers the different characteristics between MEC and cloud computing as well as the impact of latency constraint on transmission costs. Next, the advantages of genetic algorithm (GA) and particle swarm optimization (PSO) are integrated to optimize the proposed model, which utilities the fast convergence of PSO and the crossover and mutation operations of GA. Simulations using real‐world scientific workflows show the effectiveness of the proposed method for reducing data placement costs in MEC.

An Adaptive Algorithm for Image Encryption: Pseudo Random Generator with Crossover of Genetic Algorithm

An Adaptive Algorithm for Image Encryption: Pseudo Random Generator with Crossover of Genetic Algorithm

A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems

Many multi-parent crossovers have been proposed to solve specific combinatorial optimization problem and not applicable to solve other problems (i.e. cannot produce feasible solution). Only multi-parent partially mapped crossover (MPPMX) and adjacency-based crossover (ABC) have been proposed to work over different combinatorial problems. However, both MPPMX and ABC suffered from a very high computational time or poor performance. Therefore, this work proposes a novel multi-parent order crossover (MPOX) for solving several combinatorial optimization problems with reasonable amount of time. The MPOX extends the two-parent order crossover by modifying the recombination operator to recombine more than two parents and generates a new offspring. MPOX at first selects the crossover points and divides the parents into n substrings based on these points (where n is the number of parents). Then, MPOX copies a predefined number of elements from each parent into the offspring based on their order while checking the feasibility of the offspring. The performance of MPOX is tested on the traveling salesman problems and berth allocation problems, which are widely studied in the literature. Experimental results demonstrated that the MPOX significantly improves the OX in both problem domains and outperforms both ABC and MPPMX over the travelling salesman problem and the berth allocation problem with less computational time. These results indicate the effectiveness of MPOX over OX, ABC and MPPMX, and its capability for solving both problems.

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics.

Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

Adaptability Planning of Transmission Grid with Integration of High-proportion Renewable Energy

The large-scale integration of renewable energy to power grid is an important feature of future power system development, but renewable energy has strong fluctuation and high uncertainty, which will have a strong impact on power grid. In order to ensure the safe, efficient and reliable operation of power system, improve the acceptability of renewable energy in power grid planning, it is urgent to evaluate the adaptability of power grid structure to the strong fluctuation and uncertainty of renewable energy. Considering that the adaptability of power grid has a broad meaning and is difficult to quantify, this paper analyses the characteristics and actual operation state of the high-penetration renewable energy system and establishes an adaptability index series of power gird structure considering operation safety, efficiency and stability. Based on the adaptability indexes, a multi-objective transmission planning model is put forward. The improved chaotic crossover genetic algorithm and the nonlinear PCA method are used to solve the planning model. Finally, the simulation of Gaver-18 bus system demonstrates the feasibility and effectiveness of the adaptability indexes and planning model.

Using improved firefly algorithm based on genetic algorithm crossover operator for solving optimization problems

Using improved firefly algorithm based on genetic algorithm crossover operator for solving optimization problems

A genetic algorithm based task scheduling system for logistics service robots

The demand for autonomous logistics service robots requires an efficient task scheduling system in order to optimise cost and time for the robot to complete its tasks. This paper presents a Genetic algorithm (GA) based task scheduling system for a ground mobile robot that is able to find a global near-optimal travelling path to complete a logistics task of pick-and-deliver items at various locations. In this study, the chromosome representation and the fitness function of GA is carefully designed to cater for a single load logistics robotic task. Two variants of GA crossover are adopted to enhance the performance of the proposed algorithm. The performance of the scheduling is compared and analysed between the proposed GA algorithms and a conventional greedy algorithm in a virtual map and a real map environments that turns out the proposed GA algorithms outperform the greedy algorithm by 40% to 80% improvement.

Flexibility planning of transmission grid based on correlation of generation and transmission

The sharp development of power generation technology involving the clean energy boosts the grid-connected proportion of clean power such as wind and light, whereas the randomness and volatility of their outputs contribute to the strong uncertainty of multi-space-time coupling in the power system, seriously affecting the security and stability of the power grid. Hence, the traditional grid planning has been unable to meet the needs of high proportion of grid-connected clean energy. However, the flexibility of the power system can effectively reflect the system’s ability to withstand the impact of uncertainty and is of great significance to the integration of the high proportion of clean energy. Based on the original definition of flexibility, this paper proposes a flexibility index system considering supply and demand balance, grid layout and power flow distribution, and comprehensively evaluates the power system’s acceptance of clean energy. Besides, a two-level extended programming model of transmission network with flexibility index system is established. The improved chaotic crossover genetic algorithm is used to solve the planning model. The feasibility and practicability of the proposed index system and planning model are verified by the simulation of the Garver-18 node system respectively.

Attribute-based hierarchical file encryption for efficient retrieval of files by DV index tree from cloud using crossover genetic algorithm

File storing as well as retrieving is the most important security-related research area in cloud. Based on our literature survey, many researchers have developed several searchable encryption protocols. Among the several protocols, only minimum number of protocols satisfies the efficient file retrieval from the cloud. Previous research papers support the file encryption based on the keyword, secret key and random number. But in this research paper, we have proposed attribute-based encryption based on the hierarchical manner. The collections of files are encrypted under one common access layer. Compared to earlier researches, our attribute-based hierarchical file encryption scheme supports very less computation as well as storage complexity. We have constructed a new index model named data vector (DV) tree using crossover genetic algorithm which is a component of the soft computing. The DV tree is built based on the term frequency, inverse document frequency and attributes of the file. We have also developed a new key generation, encryption and decryption function for efficient retrieval of files towards the data users.

Geostatistical rock physics AVA inversion

Reservoir models are numerical representations of the subsurface petrophysical properties such as porosity, volume of minerals and fluid saturations. These are often derived from elastic models inferred from seismic inversion in a two-step approach: first, seismic reflection data are inverted for the elastic properties of interest (such as density, P-wave and S-wave velocities); these are then used as constraining properties to model the subsurface petrophysical variables. The sequential approach does not ensure a proper propagation of uncertainty throughout the entire geo-modelling workflow as it does not describe a direct link between the observed seismic data and the resulting petrophysical models. Rock physics models link the two domains. We propose to integrate seismic and rock physics modelling into an iterative geostatistical seismic inversion methodology. The proposed method allows the direct inference of the porosity, volume of shale and fluid saturations by simultaneously integrating well-logs, seismic reflection data and rock physics model predictions. Stochastic sequential simulation is used as the perturbation technique of the model parameter space, a calibrated facies-dependent rock physics model links the elastic and the petrophysical domains and a global optimizer based on cross-over genetic algorithms ensures the convergence of the methodology from iteration to iteration. The method is applied to a 3D volume extracted from a real reservoir dataset of a North Sea reservoir and compared to a geostatistical seismic AVA.

Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification

EEG signal analysis involves multi-frequency non-stationary brain waves from multiple channels. Segmenting these signals, extracting features to obtain the important properties of the signal and classification are key aspects of detecting epileptic seizures. Despite the introduction of several techniques, it is very challenging when multiple EEG channels are involved. When many channels exist, a spatial filter is required to eliminate noise and extract relevant information. This adds a new dimension of complexity to the frequency feature space. In order to stabilize the classifier of the channels, feature selection is very important. Furthermore, and to improve the performance of a classifier, more data is required from EEG channels for complex problems. The increase of such data poses some challenges as it becomes difficult to identify the subject dependent bands when the channels increase. Hence, an automated process is required for such identification.The proposed approach in this work tends to tackle the multiple EEG channels problem by segmenting the EEG signals in the frequency domain based on changing spikes rather than the traditional time based windowing approach. While to reduce the overall dimensionality and preserve the class-dependent features an optimization approach is used. This process of selecting an optimal feature subset is an optimization problem. Thus, we propose an adaptive multi-parent crossover Genetic Algorithm (GA) for optimizing the features used in classifying epileptic seizures. The GA-based approach is used to optimize the various features obtained. It encodes the temporal and spatial filter estimates and optimize the feature selection with respect to the classification error. The classification was done using a Support Vector Machine (SVM).The proposed technique was evaluated using the publicly available epileptic seizure data from the machine learning repository of the UCI center for machine learning and intelligent systems. The proposed approach outperforms other ones and achieved a high level of accuracy. These results, indicate the ability of a multi-parent crossover GA in optimizing the feature selection process in EEG classification.

MCEDA: A novel many-objective optimization approach based on model and clustering

To solve many-objective optimization problems (MaOPs) by evolutionary algorithms (EAs), the maintenance of convergence and diversity is essential and difficult. Improved multi-objective optimization evolutionary algorithms (MOEAs), usually based on the genetic algorithm (GA), have been applied to MaOPs, which use the crossover and mutation operators of GAs to generate new solutions. In this paper, a new approach, based on decomposition and the MOEA/D framework, is proposed: model and clustering based estimation of distribution algorithm (MCEDA). MOEA/D means the multi-objective evolutionary algorithm based on decomposition. The proposed MCEDA is a new estimation of distribution algorithm (EDA) framework, which is intended to extend the application of estimation of distribution algorithm to MaOPs. MCEDA was implemented by two similar algorithm, MCEDA/B (based on bits model) and MCEDA/RM (based on regular model) to deal with MaOPs. In MCEDA, the problem is decomposed into several subproblems. For each subproblem, clustering algorithm is applied to divide the population into several subgroups. On each subgroup, an estimation model is created to generate the new population. In this work, two kinds of models are adopted, the new proposed bits model and the regular model used in RM-MEDA (a regularity model based multi-objective estimation of distribution algorithm). The non-dominated selection operator is applied to improve convergence. The proposed algorithms have been tested on the benchmark test suite for evolutionary algorithms (DTLZ). The comparison with several state-of-the-art algorithms indicates that the proposed MCEDA is a competitive and promising approach.

An improved genetic algorithm for structural optimization of Au–Ag bimetallic nanoparticles

The structures are crucial for bimetallic nanoparticles (NPs) because they can determine their unique physical and chemical properties. Therefore, structures optimization of bimetallic NPs from theoretical calculation is of increasing importance for understanding their stabilities and catalytic performance. In this article, an improved genetic algorithm (IGA) is proposed to systematically investigate the structural stabilities of Au–Ag NPs. In the IGA, a layered coordinate ranking method is adopted to enhance the structural stability during initialization. Meanwhile, a difference transition fitness function is introduced to keep the population diversity and preserve the best individual of IGA. Furthermore, for improving the global searching ability and local optimization speed, a sphere-cut-splice crossover is employed to replace the classical plane-cut-splice crossover in general genetic algorithm. The performance of IGA has been compared with Monte Carlo simulation method and particle swarm optimization algorithm, the results reveal our algorithm possesses superior convergence and stability.

A distance between populations for n-points crossover in genetic algorithms

Abstract The theoretical study of Genetic Algorithms and the dynamics induced by their genetic operators is a research field with a long history and many different approaches. In this paper we complete a recently presented approach to model one-point crossover using pretopologies (or Cechtopologies) in two ways. First, we extend it to the case of n-points crossover. We extend the definition of crossover distance between populations to work for n-points crossover, proving that computing it can be performed in polynomial time for any fixed number of crossover points. Secondly, we experimentally study how the distance distribution changes when the number of crossover points increases. In particular, the average distance between a population and the optimum decreases with the increase in the number of crossover points, showing that increasing the latter can reduce the number of crossover operations needed to evolve an optimal solution.

Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem

The workforce scheduling and routing problem refers to the assignment of personnel to visits, across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise the operational cost. One of the main obstacles in designing a genetic algorithm for this problem is selecting the best set of operators that enable better GA performance. This paper presents a novel adaptive multiple crossover genetic algorithm to tackle the combined setting of scheduling and routing problems. A mix of problem-specific and traditional crossovers are evaluated by using an online learning process to measure the operator’s effectiveness. Best operators are given high application rates and low rates are given to the worse. Application rates are dynamically adjusted according to the learning outcomes in a non-stationary environment. Experimental results show that the combined performances of all the operators were better than using only one operator used in isolation. This study provided an important opportunity to advance the understanding of the best method to use crossover operators for this highly-constrained optimisation problem effectively.

METAHEURISTIC ANALYSIS APPLIED TO THE BERTH ALLOCATION PROBLEM: CASE STUDY IN A PORT CONTAINER TERMINAL

ABSTRACT The simulated annealing (SA) and genetic algorithm (GA) metaheuristics are comparatively applied to the berth allocation problem (BAP) of a port container terminal. By synthesizing the experimental studies of the BAP available in the literature, the two methods are compared when applied to the same case. For this purpose, six test problems are considered, where SA and GA are compared using four crossover operators. Once the optimal GA crossover operator is determined, computational tests are performed with the data obtained at the port terminal to compare the performance of the two metaheuristics.

ENHANCED ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS

In this paper, Enhanced Aggressive Weed Optimization (EWO) algorithm is applied to solve the optimal reactive power Problem. Aggressive Weed Optimization is a stochastic search algorithm that imitate natural deeds of weeds in colonize and detection of appropriate place for growth and reproduction. Enhanced Aggressive Weed Optimization (EWO) algorithm is based on hybridization of genetic algorithm with weed optimization algorithm which refers combination of crossover and mutation of genetic algorithm, and by the use of the cross factor new species are arisen. Proposed Enhanced Aggressive Weed Optimization (EWO) algorithm has been evaluated in standard IEEE 118 & practical 191 bus test systems. Simulation results show that our projected approach outperforms all the entitled reported algorithms in minimization of real power loss and voltage profiles are within the specified limits.

Reliability allocation model and algorithm for phased mission systems with uncertain component parameters based on importance measure

This paper presents a model to deal with reliability allocation problem of phased mission systems (PMS), especially for PMS with uncertainty in components’ parameters due to inaccurate information and different phase environments. In practice, real value of the reliability of a component may become lower than its designed value due to such uncertainty, thus making the whole system fail to meet the required reliability level. Therefore, in this paper, we present a model that incorporates the component uncertainty in the system reliability allocation process and then propose a variance-based global importance hybrid heuristic algorithm for its solution. The variance-based global importance measure is used to evaluate the importance of a component to the mission reliability of PMS while the reliabilities of all components vary randomly. The main procedures of the proposed algorithm include: (1) generate feasible solutions by roulette wheel selection method based on a global importance index; (2) improve solutions by adjusting reliability parameter values of components according to their global importance; and (3) improve solutions by crossover and mutation operations of genetic algorithm (GA). To illustrate the effectiveness of the proposed model and algorithm, two examples of PMS are presented and the allocation solutions are validated through Monte–Carlo simulation method. Finally, we compare the proposed model with a general allocation model that does not consider component uncertainty, and additionally with a cost-based heuristic algorithm and a particle swarm optimization (PSO) algorithm. Our results show that component uncertainty has significant influence on the confidence level that an allocation solution satisfies the required system reliability. Hence, it is essential to consider component uncertainty in reliability allocation process. In comparison with the cost heuristic algorithm and the PSO algorithm, the proposed algorithm is more effective in reliability allocation of PMS with uncertainty in components’ parameters.

Comparative study and optimization of the mechanical behavior of sandwich beams loaded in three point bending

The sandwich material presents a certain interest in term of rigidity and specific resistance for the aeronautic structure design. The study of this material meets always the choice problem of their constituents (coating and core) in order to find the optimal mechanical characteristic. The aim of this work is to do a comparative study of the mechanical behavior of the sandwich beams loaded in three point bending. The structures considered consist of two skins of composite material with unidirectional fibers of six plies Glass/epoxy, Carbon/epoxy or Kevlar/epoxy of stacking sequence [0°/90°]3s and [45°/-45°]3s and the core is in Alporas , Corecel, PVC, or Polyurethane foams. The different results obtained from the Matlab code showed that the correct choice of the material of the coatings and of the core that improves the mechanical behavior of the sandwich beam. In order to increase the performance of sandwich beams, in three points bending, an optimization program based on the principles of genetic algorithms has been developed to maximize the tensile strength of face yielding and face wrinkling failure modes. The equations used to evaluate the individuals in population results from the classical laminate theory with transverse shear stresses and discrete variables. Operators of genetic algorithms (selection, crossover and mutation) are applied to the children of a hundred generations. To achieve optimal solutions, the result appeared effective despite all the non-deterministic nature of genetic algorithms. But, to achieve maximum effectiveness, it’s important to choose smartly the parameters of genetic algorithms depending on the nature of the problem studied and the mechanical characteristics of the function to be optimized.

Extended Genetic Algorithm for solving open-shop scheduling problem

Open-shop scheduling problem (OSSP) is a well-known topic with vast industrial applications which belongs to one of the most important issues in the field of engineering. OSSP is a kind of NP problems and has a wider solution space than other basic scheduling problems, i.e., Job-shop and flow-shop scheduling. Due to this fact, this problem has attracted many researchers over the past decades and numerous algorithms have been proposed for that. This paper investigates the effects of crossover and mutation operator selection in Genetic Algorithms (GA) for solving OSSP. The proposed algorithm, which is called EGA_OS, is evaluated and compared with other existing algorithms. Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values.