scholarly journals A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 550 ◽  
Author(s):  
Hui Nies ◽  
Zalmiyah Zakaria ◽  
Mohd Mohamad ◽  
Weng Chan ◽  
Nazar Zaki ◽  
...  
Keyword(s):  
Objective Function ◽  
Gene Clusters ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Biological Functions ◽  
Clustering Techniques ◽  
Leukemia Dataset ◽  
Clustering Validation ◽  
Cluster Quality ◽  
Grid Based

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

A novel approach to increase the share of renewable purchase obligation for planning of distribution network including grid scale energy storage

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jitendra Singh Bhadoriya ◽  
Atma Ram Gupta
Keyword(s):  
Energy Storage ◽  
Objective Function ◽  
Distributed Generation ◽  
Carbon Emissions ◽  
Distribution Network ◽  
Clean Energy ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Novel Approach ◽  
Demand Models

Abstract In recent times, producing electricity with lower carbon emissions has resulted in strong clean energy incorporation into the distribution network. The technical development of weather-driven renewable distributed generation units, the global approach to reducing pollution emissions, and the potential for independent power producers to engage in distribution network planning (DNP) based on the participation in the increasing share of renewable purchasing obligation (RPO) are some of the essential reasons for including renewable-based distributed generation (RBDG) as an expansion investment. The Grid-Scale Energy Storage System (GSESS) is proposed as a promising solution in the literature to boost the energy storage accompanied by RBDG and also to increase power generation. In this respect, the technological, economic, and environmental evaluation of the expansion of RBDG concerning the RPO is formulated in the objective function. Therefore, a novel approach to modeling the composite DNP problem in the regulated power system is proposed in this paper. The goal is to increase the allocation of PVDG, WTDG, and GSESS in DNP to improve the quicker retirement of the fossil fuel-based power plant to increase total profits for the distribution network operator (DNO), and improve the voltage deviation, reduce carbon emissions over a defined planning period. The increment in RPO and decrement in the power purchase agreement will help DNO to fulfill round-the-clock supply for all classes of consumers. A recently developed new metaheuristic transient search optimization (TSO) based on electrical storage elements’ stimulation behavior is implemented to find the optimal solution for multi-objective function. The balance between the exploration and exploitation capability makes the TSO suitable for the proposed power flow problem with PVDG, WTDG, and GSESS. For this research, the IEEE-33 and IEEE-69 low and medium bus distribution networks are considered under a defined load growth for planning duration with the distinct load demand models’ aggregation. The findings of the results after comparing with well-known optimization techniques DE and PSO confirm the feasibility of the method suggested.

A new Hybrid Taguchi-salp swarm optimization algorithm for the robust design of real-world engineering problems

2021 ◽  
Vol 63 (2) ◽  
pp. 157-162
Author(s):  
Ali Rıza Yıldız ◽  
Mehmet Umut Erdaş
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Structural Design ◽  
Design Problem ◽  
Optimization Techniques ◽  
Stress Constraint ◽  
Element Analysis ◽  
Salp Swarm Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Swarm Algorithm

Abstract In this paper, a new hybrid Taguchi salp swarm algorithm (HTSSA) has been developed to speed up the optimization processes of structural design problems in industry and to approach a global optimum solution. The design problem is posed for the shape optimization of a seat bracket with a mass objective function and a stress constraint. Objective function evaluations are based on finite element analysis, while the response surface method is used to obtain the equations necessary for objective and constraint functions. Recent optimization techniques such as the salp swarm algorithm, grasshopper optimization algorithm and, Harris hawks optimization algorithm are used to compare the performance of the HTSSA in solving the structural design problem. The results show the hybrid Taguchi salp swarm algorithm’s ability and the superiority of the method developed for optimum product design processes.

Data Clustering Using Sine Cosine Algorithm

2017 ◽  
pp. 715-726 ◽  
Author(s):  
Vijay Kumar ◽  
Dinesh Kumar
Keyword(s):  
Real Life ◽  
Quality Measures ◽  
Search Space ◽  
Search Method ◽  
Local Optima ◽  
Encoding Scheme ◽  
Clustering Techniques ◽  
Sine Cosine Algorithm ◽  
Cluster Quality ◽  
Optimal Cluster

The clustering techniques suffer from cluster centers initialization and local optima problems. In this chapter, the new metaheuristic algorithm, Sine Cosine Algorithm (SCA), is used as a search method to solve these problems. The SCA explores the search space of given dataset to find out the near-optimal cluster centers. The center based encoding scheme is used to evolve the cluster centers. The proposed SCA-based clustering technique is evaluated on four real-life datasets. The performance of SCA-based clustering is compared with recently developed clustering techniques. The experimental results reveal that SCA-based clustering gives better values in terms of cluster quality measures.

PMU Placement Optimization for Fault Observation Using Different Techniques

2021 ◽  
pp. 196-220
Author(s):  
Hamid Bentarzi
Keyword(s):  
Cuckoo Search ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Placement Optimization ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Pmu Placement ◽  
Teaching Learning ◽  
Full Network

This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.

Models for Internal Clustering Validation Indexes Based on Hadoop-MapReduce

2020 ◽  
Vol 11 (3) ◽  
pp. 42-67
Author(s):  
Soumeya Zerabi ◽  
Souham Meshoul ◽  
Samia Chikhi Boucherkha
Keyword(s):  
Clustering Algorithms ◽  
Large Data ◽  
Optimal Number ◽  
Data Sets ◽  
Data Set ◽  
Number Of Clusters ◽  
Distributed Models ◽  
Hadoop Mapreduce ◽  
Distributed Solutions ◽  
Clustering Validation

Cluster validation aims to both evaluate the results of clustering algorithms and predict the number of clusters. It is usually achieved using several indexes. Traditional internal clustering validation indexes (CVIs) are mainly based in computing pairwise distances which results in a quadratic complexity of the related algorithms. The existing CVIs cannot handle large data sets properly and need to be revisited to take account of the ever-increasing data set volume. Therefore, design of parallel and distributed solutions to implement these indexes is required. To cope with this issue, the authors propose two parallel and distributed models for internal CVIs namely for Silhouette and Dunn indexes using MapReduce framework under Hadoop. The proposed models termed as MR_Silhouette and MR_Dunn have been tested to solve both the issue of evaluating the clustering results and identifying the optimal number of clusters. The results of experimental study are very promising and show that the proposed parallel and distributed models achieve the expected tasks successfully.

An Elitist Ants-Search Based Method for Optimum Synthesis of Rigid-Linkage Mechanisms

2015 ◽  
Author(s):  
Nadim Diab ◽  
Ahmad Smaili
Keyword(s):  
Objective Function ◽  
Linear Constraints ◽  
Optimization Techniques ◽  
Path Generation ◽  
Generation Task ◽  
Optimum Synthesis ◽  
Structural Error ◽  
Constant Quantity ◽  
Future Work ◽  
Exploration Exploitation

Mechanical linkages are widely used in the industry and the synthesis of such mechanisms may require optimization depending on the number of precision positions required. Many intelligent optimization techniques (Genetic, Tabu, Simulated Annealing, etc) have been proposed in the literature, one of them being the Ant-Search which was first proposed by the authors in 2007. In this paper, a Modified Ant-Search (MAS) technique is proposed to optimize the synthesis of a four-bar mechanism with a path generation task. Two major improvements are applied over the previous algorithm: ants pheromone update and exploration/exploitation techniques are both modified. Unlike the previous work where a constant quantity of pheromones was added during each iteration, in this paper, the pheromone deposit rate is proportional to the error of the objective function. Such a modification in the pheromone update rule is expected to differentiate between the behaviors of different ants and better govern their motion in the subsequent iterations. Moreover, the second major improvement targets the exploration/exploitation techniques followed by the ants. Unlike the previous work where exploration dominates during the early iteration stages and exploitation during the late ones, this work implements a more dynamic strategy where ants enter and leave the exploration/exploitation processes as governed by parameters related to the objective function error and pheromone deposit levels. Such modifications applied to the Ant-Search (AS) technique are expected to ensure a better chance of converging to a global minimum. The MAS technique is applied for a few path generation tasks with prescribed timing along with a set of linear constraints. Results are compared with previous work in the literature where the newly proposed technique showed appreciable improvement as evaluated by the structural error objective function. Future work possibilities are also introduced.

Three-dimensional optimum design of the cooling lines of injection moulds based on boundary element design sensitivity analysis

2002 ◽  
Vol 216 (7) ◽  
pp. 1067-1071 ◽  
Author(s):  
H Zhou ◽  
D Li ◽  
S Cui
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Boundary Element ◽  
Optimum Design ◽  
Three Dimensional ◽  
Design Sensitivity ◽  
Boundary Integral ◽  
Optimization Techniques ◽  
Design Sensitivity Analysis ◽  
Design Variables

A three-dimensional numerical simulation using the boundary element method is proposed, which can predict the cavity temperature distributions in the cooling stage of injection moulding. Then, choosing the radii and positions of cooling lines as design variables, the boundary integral sensitivity formulations are deduced. For the optimum design of cooling lines, the squared difference between the objective temperature and the temperature of the cavity is taken as the objective function. Based on the optimization techniques with design sensitivity analysis, an iterative algorithm to reach the minimum value of the objective function is introduced, which leads to the optimum design of cooling lines at the same time.

scholarly journals Comparative Study of Popular Objective Functions for Damping Power System Oscillations in Multimachine System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Naz Niamul Islam ◽  
M. A. Hannan ◽  
Hussain Shareef ◽  
Azah Mohamed ◽  
M. A. Salam
Keyword(s):  
Power System ◽  
Objective Function ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Stable Region ◽  
Objective Functions ◽  
Linearized Model ◽  
Oscillation Damping ◽  
Power System Oscillations ◽  
Damping Controller

Power oscillation damping controller is designed in linearized model with heuristic optimization techniques. Selection of the objective function is very crucial for damping controller design by optimization algorithms. In this research, comparative analysis has been carried out to evaluate the effectiveness of popular objective functions used in power system oscillation damping. Two-stage lead-lag damping controller by means of power system stabilizers is optimized using differential search algorithm for different objective functions. Linearized model simulations are performed to compare the dominant mode’s performance and then the nonlinear model is continued to evaluate the damping performance over power system oscillations. All the simulations are conducted in two-area four-machine power system to bring a detailed analysis. Investigated results proved that multiobjective D-shaped function is an effective objective function in terms of moving unstable and lightly damped electromechanical modes into stable region. Thus, D-shape function ultimately improves overall system damping and concurrently enhances power system reliability.

scholarly journals Effect of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines for BI- Criteria Objectives

2019 ◽  
Vol 9 (1) ◽  
pp. 2827-2831
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Parallel Machines ◽  
Optimization Techniques ◽  
Computational Time ◽  
Planning And Scheduling ◽  
Multi Objective ◽  
Mutation Operators ◽  
Crossover Probability ◽  
Wide Range

Optimization of multi objective function gain the importance in the scheduling process. Many classical techniques are available to address the multi objective functions but the solutions yield the unsatisfactory results when the problem becomes complex and large. Evolutionary algorithm would be the solution for such problems. Genetic algorithm is adaptive heuristic search algorithms and optimization techniques that mimic the process of natural evolution. Genetic algorithms are a very effective way of obtaining a reasonable solution quickly to a complex problem. The genetic algorithm operators such as selection method, crossover method, crossover probability, mutation operators and stopping criteria have an effect on obtaining the reasonably good solution and the computational time. Partially mapped crossover operators are used to solve the problem of the traveling salesman, planning and scheduling of the machines, etc., which are having a wide range of solutions. This paper presents the effect of crossover probability on the performance of the genetic algorithm for the bi-criteria objective function to obtain the best solution in a reasonable time. The simulation on a designed genetic algorithm was conducted with a crossover probability of 0.4 to 0.95 (with a step of 0.05) and 0.97, found that results were converging for the crossover probability of 0.6 with the computational time of 3.41 seconds.

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