Fuzzy-based Genetic Algorithm Research Articles

Protein Prediction using Dictionary Based Regression Learning

Research Objectives: Molecular genetic data is managed by the information technology known as bioinformatics. Major concept involved in bioinformatics is a protein sequence. Amino acids bonded with peptide bond constitute the sequence of Protein and it is very essential to lead life. To predict sequence of amino acid, primary sequence obtains amino sequence folding and structures prediction. Research Novelty: In this manuscript, dictionary based regression learning and fuzzy genetic algorithm is proposed for protein prediction from structural analysis (DRL-FGA-PD-SA). In this input data are taken from Kaggle domain dataset. The extraction of protein features from given data is made through Kernel Matrix (KM) which extracts composition of amino acids, composition of dipeptide, composition of pseudo-amino-acid, composition of functional domain and distance-based features. Then fuzzy based genetic algorithm (FGA) update the selected features for classification of protein and the features are clustered. Finally, dictionary based regression learning (DRL) predicts the class of protein with conversion of values either 0’s or 1’s. Research Conclusions: The proposed method is executed on MATLAB. Here evaluation metrics as sensitivity, precision, f-measure, specificity, accuracy and error rate are outlined. Then the performance of the proposed DRL-FGA-PD-SA method provides 22.08%, 24.03%, 34.76% higher accuracy, 23.34%, 26.45%, 34.44% higher precision, compared with the existing systems such assdeep learning methods in protein structure prediction (FFNN-RNN-PD-SA), deep learning technique for protein structure prediction and protein design (DNN-PD-SA) and improved protein structure prediction using potentials from deep learning (DNN-SGDA-PD-SA) respectively.

The fuzzy-based systems in the communication between a human and a humanoid robot

Abstract The communication between a human and a humanoid robot is a real challenge for the researchers in the field of the robotics. Despite of the progress in the acoustic modelling and in the natural languages the humanoid robots are overtaken by the humans when the humanoid robots are engaged in the real life because the speech and the human emotions are extremely ambiguous due to the noises and the external audio events from the robot’s environment. The humans assign a correct interpretation to the perceived ambiguous signal, but the humanoids robots cannot interpret the ambiguous signal. The most common software used in the interpretation of the ambiguous signal is a fuzzy based software. The artificial neuro-fuzzy inference system, shortly known as ANFIS is the emotion recognition system based on the fuzzy sets which acts as the thalamus of the human brain and it is responsible for the sensorial perception of the humanoid robot. Our goal in this work is to create the fuzzy-based sound signals software and the fuzzy-based genetic algorithm with high performance in the communication between the human and the humanoid robots which help the humanoid robots to think, to understand the human speech and the human emotions and all the ambiguous signals from the robot’s environment in a way that it is distinguishable for every humanoid robot as the human.

Fuzzy genetic algorithm-based model for bullwhip effect reduction in a multi-stage supply chain

Synchronisation among supply chain (SC) stages is essential, but difficult to achieve, which results in a bullwhip effect. This paper proposes an interactive fuzzy-based genetic algorithm approach for reducing bullwhip effect by minimising total SC cost as well as to determine optimal ordering quantities in a multi-stage, multi-period SC using fuzzy logic combined with genetic algorithm. To face the uncertainty, forecasted customer demand and other SC cost related parameters are considered as uncertain parameters which are modelled through triangular fuzzy membership function. We used the strategy of simultaneously minimising the most possible value, the most pessimistic value and the most optimistic value of the total costs. Finally, a real-life case study is solved with the help of Matlab software to illustrate the usefulness of the approach where we employed different unique genetic algorithm parameters and compared the result obtained with existing policy and using only genetic algorithm.

AGV routing and motion planning in a flexible manufacturing system using a fuzzy-based genetic algorithm

Autonomous-guided vehicles (AGVs) are becoming increasingly prevalent and already helping in a variety of activities, ranging from space exploration to domestic housework. Recent advances in the design of sensors, motors and microelectromechanical systems are bringing us closer to the realization of autonomous multi robot systems which perform complex tasks. In this work, we explored the problem of vehicle routing and motion planning for a fleet of AGVs in a flexible manufacturing system (FMS). Considering concurrently fuzzy demands associated with the workstations and fuzzy travel distances while moving between workstations, the problem is addressed in the context of uncertainty both in demands and travel times. The proposed motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. Furthermore, in order to take into account the fuzziness which may arise in the FMS, the proposed planner is integrated with fuzzy theory on fuzzy sets and fuzzy numbers. The efficiency of the solution procedure is demonstrated through numerical examples.

An experimental investigation of performance and emission of thumba biodiesel using butanol as an additive in an IDI CI engine and analysis of results using multi-objective fuzzy-based genetic algorithm.

The present work studies the effect of butanol in thumba (Citrullus colocynthis) biodiesel in an IDI CI engine at varying percentages of 5 and 10% in 15 and 10% thumba biodiesel respectively with 80% diesel in each blend. Another blend was introduced with 80% diesel and 20% biodiesel without any additive. The experiment was conducted in a single cylinder four-stroke IDI CI engine at 1500rpm varying from 25% to full-load (100%) conditions. The results showed diesel with less bio diesel and higher butanol in percentage gives good performance and emission compared to diesel at higher loads. Blend containing 10% bio diesel, 10% butanol, and 80% diesel (D80B10Bu10) showed higher cylinder pressure, heat release rate, BThE, and less NOx. Biodiesels gave less UHC, CO emissions. In this work, multi-objective fuzzy-based genetic algorithm was introduced for the best fit result. Four parameters were used for optimization (BSFC, BThE, CO, NOx). The result from genetic algorithm was taken for validation and the optimized result was found adequate after validation.

Optimal Overcurrent Relay Coordination in Interconnected Networks by Using Fuzzy-Based GA Method

A new objective function (OF) has been proposed for mathematical formulation of directional overcurrent (OC) relay coordination in interconnected networks. The fuzzy-based genetic algorithm (GA) is applied to optimize the proposed OF for optimal coordination of OC relays. The defined fuzzy rules update the weighting factors of OF during the simulation. The miscoordination problem of OC relays is solved while decreasing the operating time of the relays. The proposed method is implemented in three different networks and the simulation results have been compared with previous studies in order to illustrate the accuracy and efficiency of the proposed method to coordinate the directional OC relays with both discrete and continuous time setting multipliers. The results have also been compared with the results of other optimization methods. Considering the coefficients of OC characteristic curves as the OF variables ensures that the proposed method has no limitation for the types of characteristic curves which will be utilized. Presenting the new term in OF, the performance of the proposed method has not been affected by the size of the networks.

A possibilistic environment based particle swarm optimization for aggregate production planning

Development of an Aggregate Production Plan (APP), the top most level in a hierarchical production planning system, is a difficult task, especially when input and other production planning parameters are uncertain because of their inherent impreciseness. This therefore makes generation of a master production schedule highly complex. Regarding this point, in this paper, we present a scheme of a multi-period and multi-product APP which is formulated as an integer linear programming model. The proposed approach uses a triangular possibility distribution for handling all the imprecise operating costs, demands, and also for the capacity data. The proposed approach uses the strategy of simultaneously minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining lower total costs and minimizing the risk of obtaining higher total costs. A modified variant of a possibilistic environment based particle swarm optimization (PE-PSO) approach is used to solve the APP model. A numerical model for demonstrating the feasibility of the proposed model is also carried out. In the computational study, the considered case study data were experimented with and analyzed to evaluate the performance of the PE-PSO over both a standard genetic algorithm (GA) and a fuzzy based genetic algorithm (FBGA). The experimental results demonstrate that the PE-PSO variant provides better qualities in the aspects of its accuracy when compared to the other two algorithms.

Fuzzy-GA inference technique to control three-input jet ejector system

Jet ejectors find many applications in thermal systems including refrigeration, heat treatment, and fuel injection. In this study, a three-input jet ejector model is developed and the resulting non-linear governing equations are used to design the corresponding controller. A fuzzy-based genetic algorithm (fuzzy-GA) technique is used to represent and control the system using linguistic fuzzy sets, which are tuned using a genetic algorithm code. The proposed controller is tested on different fluid types where simulation results showed the effectiveness and robustness of the technique.

Solving an Aggregate Production Planning Problem by Fuzzy Based Genetic Algorithm (FBGA) Approach

This paper work demonstrated an interactive Fuzzy Based Genetic Algorithm (FBGA) approach for solving a two products & two periods aggregate production planning (APP) with some vulnerable managerial constraints like imprecise demands, variable manufacturing costs etc. This proposed approach used the strategy of simultaneously minimizing the most possible value, the most pessimistic value & also most optimistic value of the imprecise total costs in lieu of some strong resource constraints. Another important purpose of this study is to derive & observe the variations along with the scope of the imprecise total cost, maximizing the possibility of obtaining lower total costs and also minimizing the risk of obtaining higher total costs. Here the authors employed different unique genetic algorithm parameters scrupulously for solving nondeterministic polynomials problems like APP problems. For reinforcing & accelerating the decision making for the decision maker a case study was considered in a Ready Made garment company in Bangladesh. Consequently, the proposed FBGA approach yields an efficient APP compromise solution and could be efficient for large scale problems.

User-Centric Optimization for Constraint Web Service Composition using a Fuzzyguided Genetic Algorithm System

Service-Oriented Applications (SOA) are being regarded as the main pragmatic solution for distributed environments. In such systems, however each service responds the user request independently, it is essential to compose them for delivering a compound value-added service. Since, there may be a number of compositions to create the requested service, it is important to find one which its properties are close to user's desires and meet some non-functional constraints and optimize criteria such as overall cost or response time. In this paper, a user-centric approach is presented for evaluating the service compositions which attempts to obtain the user desires. This approach uses fuzzy logic in order to inference based on quality criteria ranked by user and Genetic Algorithms to optimize the QoS-aware composition problem. Results show that the Fuzzy-based Genetic algorithm system enables user to participate in the process of web service composition easier and more efficient.

Social welfare maximization with fuzzy based genetic algorithm by TCSC and SSSC in double-sided auction market

This paper presents a fuzzy-based genetic algorithm to maximize total system social welfare by best the placement and sizing of TCSC and SSSC devices, considering their investment cost in a double-sided auction market. To introduce more accurate modeling, the valve loading effects are incorporated into the conventional quadratic smooth generator cost curves. In addition, quadratic consumer benefit functions are integrated into the objective function to guarantee that locational marginal prices charged at the demand buses are less than, or equal to, the DisCos benefit, earned by selling the power to retail customers. The proposed approach utilizes fuzzy-based genetic algorithms for optimal scheduling of GenCos and DisCos, as well as optimal placement and sizing of SSSC and TCSC units. In addition, the Newton–Raphson approach is used to minimize the mismatch of the power flow equation. Simulation results on the modified IEEE 14-bus and IEEE 30-bus test systems (with/without line flow constraints, before and after the compensation) are used to examine the impact of SSSC and TCSC on total system social welfare improvement versus their cost. To validate the accuracy of the proposed method, several case studies are presented and simulation results are compared with those generated by genetic and Sequential Quadratic Programming (SQP) approaches.

USER-CENTRIC OPTIMIZATION FOR CONSTRAINT WEB SERVICE COMPOSITION USING A FUZZYGUIDED GENETIC ALGORITHM SYSTEM

Service-Oriented Applications (SOA) are being regarded as the main pragmatic solution for distributed environments. In such systems, however each service responds the user request independently, it is essential to compose them for delivering a compound value-added service. Since, there may be a number of compositions to create the requested service, it is important to find one which its properties are close to user’s desires and meet some non-functional constraints and optimize criteria such as overall cost or response time. In this paper, a user-centric approach is presented for evaluating the service compositions which attempts to obtain the user desires. This approach uses fuzzy logic in order to inference based on quality criteria ranked by user and Genetic Algorithms to optimize the QoS-aware composition problem. Results show that the Fuzzy-based Genetic algorithm system enables user to participate in the process of web service composition easier and more efficient.

A Fuzzy-based Genetic Algorithm for Social Welfare Maximization by Placement and Sizing of Static Synchronous Series Compensator

This article presents a fuzzy-based genetic algorithm to maximize total social welfare and alleviate congestion by placement and sizing of one static synchronous series compensator device, considering its investment cost in a double-sided auction market. The generating units cost curves are considered to be quadratic with sine components to show the impacts of valve point loading. By adding the valve point effect, the model presents non-differentiable and convex regions that challenge most gradient-based optimization algorithms. In addition, the impact of distribution companies on the social welfare maximization and congestion management is presented as a quadratic function. The proposed approach makes use of the fuzzy-based genetic algorithm to optimal schedule generating companies and distribution companies and setting the static synchronous series compensator location and its size while the Newton–Raphson algorithm minimizes the mismatch of the power flow equations. Simulation results on the modified IEEE 14- and 30-bus test systems (with/without line flow constraints, before/after compensation) are used to examine the impacts of the static synchronous series compensator on the total system social welfare improvement versus its cost. Several cases are considered to test and validate the consistency of detecting best solutions. Simulation results are compared to solutions obtained by the genetic algorithm and sequential quadratic programming approach, which has been used in MATPOWER (available on-line; see [1]). The aim of this article is the utilization of static synchronous series compensator for the social welfare maximization problem considering the impact of valve point loading effect on the operation point of the generating companies by inclusion of fuzzy rules in the genetic algorithm to guarantee fast convergence for locating/sizing the static synchronous series compensator. The proposed method shows the benefits of the static synchronous series compensator in a deregulated power market and demonstrates how it can be utilized by the independent system operator to improve the total social welfare and prevent congestion.

A hybrid stock trading system for intelligent technical analysis-based equivolume charting

This paper presents the use of an intelligent hybrid stock trading system that integrates neural networks, fuzzy logic, and genetic algorithms techniques to increase the efficiency of stock trading when using a volume adjusted moving average (VAMA), a technical indicator developed from equivolume charting. For this research, a neuro–fuzzy-based genetic algorithm (NF-GA) system utilizing a VAMA membership function is introduced. The results show that the intelligent hybrid system takes advantage of the synergy among these different techniques to intelligently generate more optimal trading decisions for the VAMA, allowing investors to make better stock trading decisions.