Fuzzy GA Research Articles

A Combined Fuzzy AHP and GA Approach to Machine Scheduling Problems from an Environmental Perspective

With increasing concerns about the need for environmental protection and reduction of energy consumption, enterprises have to demonstrate their capabilities in lowering resource consumption by enhancing the efficiency of their systems. Although some approaches to quantifying the environmental burden generated by a product or service system such as life cycle assessment (LCA) and carbon auditing have been developed, expert judgments are often required to implement them. From an industry’s perspective, small- and medium-sized enterprises need an efficient tool to determine the best solution when considering various attributes simultaneously. Thus, a combination of fuzzy analytical hierarchy process and genetic algorithm has been introduced to solve scheduling problems and support the decision-making process. This study aims to effectuate the green scheduling on optimized machine-task assignments with fuzzy evaluation. The proposed approach is illustrated using a case example from a centralized dishwashing company. Results show that the global warming potential value can be reduced by 1.86% and the cost of operation is slightly increased by only 1.28%. The result of the proposed approach is presented simply in the form of machine-task assignments with optimized environmental impact values and associated costs. Therefore, no further result interpretation by environmental experts is required. This study can be a reference for government policymakers in formulating policies to synthesize operation optimization and business sustainability.

Global strong domination in a- cut diminish fuzzy graph Ga(sa, µa) by a- cut strong arc

Global strong domination in a- cut diminish fuzzy graph Ga(sa, µa) by a- cut strong arc

Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0

Recently, Industry 4.0 facilitates implementing several modular smart factories particularly the Cyber-Physical System. Due to enhanced growth in the Cyber-Physical System, privacy and security issues turned out to be the most significant factor all over the world. This paper demonstrates a complete co-design approach meant for integrating the cyberspace and physical space of a cyber-physical system. Various strategies and models regarding cyber and physical space are established in CPS. Apart from numerous co-design strategies, there are several parameters yet to be resolved and established. It becomes complicated and tricky to examine and explore the extremely best value since these parameters make up a very huge space. Therefore, a metaheuristic algorithm such as improved Fuzzy Harmonic Search Algorithm is proposed to optimize the control parameters so as to obtain a feasible solution. Also, this approach minimizes the cost function using Maximum Allowable Delay Bound (MADB) when subjected to several constraints such as the Sampling period, Horizon length, Routing graph, and scheduling table. Moreover the comparative analyses of various approaches such as Fuzzy Harmony Search (FHS) algorithm, Harmony Search (HS) algorithm, Grey Wolf Optimization Algorithm (GWO), Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm as well as Fuzzy Genetic Algorithm (Fuzzy GA) are evaluated to examine the performances of the proposed approach. A testbed is organized for evaluation and exploration in a manufacturing atmosphere. The result reveals that this proposed approach provides enhanced control performance and communication reliability even under very harsh environmental habitat.

An Extensive Literature Review and New Proposal on Optimal Capacitor Placement in Distribution Systems

The main goal of power utilities is to supply reliable and quality power to the end-users and fulfill their total demands at all possible locations. Most of the loads are connected in the distribution systems are inductive. The excessive reactive power demand over the distribution network causes tremendous reactive power losses and changes the voltage profile, hence the system's reliability. Shunt Capacitor Bank (SCB) is widely used in the distribution system for reactive power support, voltage profile, and system performance improvement. But there are some challenges to employ SCB in the distribution network; among them, ensuring the most optimum location and size is a big challenge to get the maximum benefits. Some existing techniques showed better loss reduction but needed either larger SCBs sizes or cause improper node voltage. In this research study, the first section provides an extensive literature review of optimal SCBs placement and sizing. Later on, a new technique called Combinatorial Method has been developed for sizing and sitting of optimal Shunt Capacitors to reduce the distribution loss significantly. The developed method was tested for different case studies using Indian practical 22-bus and IEEE-69-bus network. The results were compared with DSA, Fuzzy GA, and TLBO method and found better distribution feeder loss minimization and voltage profile improvement.

A Comparative Performance Analysis of Indirect Vector Controlled Induction Motor Drive Using Optimized AI Techniques

This paper exhibits a point by point comparison between Neuro Fuzzy and Genetic Algorithm GA based control systems of Induction Motor drive, underlining favorable circumstances and drawbacks. Industries are advancing and upgrading generation line to enhance efficiency and quality. Induction machines are considered by nonlinear, time varying dynamics, inaccessibility of few states and thus can be considered as a challenging issue. In this paper, a novel method using modified GA is presented to limit electric losses of Induction Motor and it is compared with Neuro Fuzzy Controller. GA is a subordinate of AI, whose principle relies upon Darwin’s theory—struggle for existence and the survival of the fittest. The technique for deciding the gain parameters of PI controller utilizing GA whose output is utilized to control the torque applied to the Induction Motor in this way controlling its speed. The gains of PI controller are improved with the assistance of GA to upgrade the performance of IM drive. The results are simulated in MATLAB Simulink and are related with the conventional PI controller and Adaptive Neuro Fuzzy controller (NFC). NFC is less complicated and gives great speed precision yet GA based PI controller produces significantly reduced torque and speed ripples compared with other controllers, in this way limiting losses in IM drives.

A Fuzzy Ga Based STATCOM for Power Quality Improvement

This paper deals with various power issues such as voltage sag, swell, harmonics, and surges using static synchronous compensator (STATCOM).The conventional controller suffers from uncertain parameters and non-linear qualities. However they are computationally inefficient extending to optimize the fuzzy controller (FC) parameters, since they exhaustively search the optimal values to optimize the objective functions. To overcome this drawback, a genetic algorithm (GA) based Fuzzy controller parameter optimization is presented in this paper. The GA algorithm is used to find the optimal fuzzy parameters for minimizing the objective functions. The feasibility of the proposed GA technique for distribution systems to improve the sag and total harmonic distortion (THD) as major power quality indices in sensitive loads at fault conditions has been simulated and tested. Therefore, the multi-objective optimization algorithm is considered in order to attain a better performance in solving the related problems.

Poisson's ratio prediction through dual stimulated fuzzy logic by ACE and GA-PS

Poisson's ratio is one of the most important rock mechanical parameters having significance in both planning and post analysis of wellbore operations. Laboratory measurement of this parameter covers a broad range of costs, including sidewall sampling, preservation, and laboratory tests. This study proposes an improved strategy, called dual stimulated fuzzy logic by ACE and GA-PS for determining Poisson's ratio from conventional well log data in a rapid, precise, and cost-effective way. Firstly, conventional well log data are transformed to a higher correlated data space with Poisson's ratio through the use of alternative condition expectation (ACE) algorithm. This step simplifies the convoluted space of the problem and makes it easier to solve for fuzzy logic. Subsequently, transformed conventional well log data are fed to fuzzy logic model. To ensure that optimal fuzzy model is constructed, a hybrid genetic algorithm-pattern search (GA-PS) technique is employed for extracting fuzzy clusters (or rules). This step sets fuzzy logic to its optimal performance. The propounded strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. A comparison between present model and previous models showed superiority of current study. • Dual stimulated fuzzy logic was used for Poisson's ratio prediction. • First, problem space is simplified by ACE for fuzzy logic. • Then, structure of fuzzy model is optimized by GA. • Integration of fuzzy logic, ACE, and GA develops an improved model. • This study surpasses previous methods in terms of R -square and MSE.

Fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy GA.

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.

Power system damping using GA based fuzzy controlled TCSC device

In introduction, this paper investigates the application of TCSC hybrid controller for improving damping inter-area modes of oscillations. The proposed controller comprised fuzzy logic proportional and derivative controller with the conventional proportional and integral controller (FUZZY PD + PI) turn by genetic algorithm techniques. Approaching the proposed control scheme implements the characteristics of the GA’s global optimization to optimize the FPID control parameters, KP, KI and K to obtain the best control effect by minimizing the integral time square error online. The performance of the proposed hybrid control in increasing the damping of local and inter-area modes of oscillation is demonstrated in a two area four machine 11-bus systems and a practical reduce system of south Malaysian power network called TNB 25-bus system. Results shows that this type of hybrid control is proved to be more robust and effective in damping electromechanical oscillations of the power systems in comparison with the conventional PID regulators used for TCSC control. The GA optimization technique is also found to be effective and efficient in tuning the FPID parameters. Key words: TCSC, power system oscillations, fuzzy logic control, GA and PID.

Accelerating fuzzy genetic algorithm for the optimization of steel structures

In this paper, the combination of a genetic algorithm and fuzzy logic is used for different types of optimization problems (sizing, topology and geometry) in steel structures. The primary objective is to introduce a new fitness function that uses a real value for the overall satisfaction parameter. By considering a real value instead of a random value for the overall satisfaction parameter, the probability of reaching the global optimum increases. In addition, to decrease the Computation time and to accelerate the convergence rate in the optimization process, a penalty function is added to the fitness function. Also, in spite of the previous methods, fuzzy GA is applied from the first generation of optimization problems. Furthermore, for this process, a similar bilinear membership function is used for the objective function and the constraints. The efficiency and robustness of the proposed approach is demonstrated by several illustrative examples and the results are compared to those of previous studies.

Research on Construction Method of Optimization Model between Customer Requirements and Product Technical Characteristics Configuration

The degree of satisfying the customer requirements was determined by the diversity of product function and form, in order to improve the satisfaction, the mapping relation between customer requirements and technical characteristics should be analyzed to construct configuration optimization model. This paper integrated triangular fuzzy number, BP network and GA to propose a method of constructing optimization model of product technical characteristics configuration corresponding to customer requirements, the model was established based on customer transaction data history. This method acquired the information of customer requirements and preference and that of purchased products’ function and form characteristic by analyzing and extracting customer transaction information, then applied BP network to express the mapping relations between customer requirements and product technical characteristics. The optimization model took the cost and time as constraint with the customer requirements as target, finally used genetic algorithm with MATLAB to solve. The electric bicycle was taken to illustrate the build process.

Hybrid genetic-fuzzy algorithm for volt/var/total harmonic distortion control of distribution systems with high penetration of non-linear loads

A hybrid genetic-fuzzy algorithm (GA-Fuzzy) is proposed for optimal volt/var/total harmonic distortion (THD) control in distorted distribution systems serving non-linear loads. Load interval division (over a 24 h period) and optimal scheduling of load tap changer and switched shunt capacitors for simultaneously minimising energy losses and improving the power quality (as recommended by the IEEE 519 and IEC 61000 standards) are performed using GAs with fuzzy reasoning. The non-linear load flow is solved using a decoupled harmonic power flow algorithm. The integration of fuzzy rules and GA enables the approach to maintain the promising chromosomes and offer further improvement of the near-global solution. The IEEE 30-bus and 123-bus distribution systems with a number of harmonic generating loads are selected for the analyses. Simulation results using GA and GA-Fuzzy optimisation approaches are presented and compared for sinusoidal and non-sinusoidal treatments of the systems to demonstrate the advantages of the proposed hybrid approach.

An Efficient Fuzzy-GA Flow Control of Turbine Compressor System: A Process Control Case Study

In this paper, an optimal control for gas turbine and compressor system using fuzzy logic, genetic algorithms have been suggested. Gas turbine compressor system is most widely used in chemical industries. The primary objective of this kind of setup is to control the flow rate of high pressure steam. Different controller architecture has been taken up to control the flow of gas. Firstly, a conventional PID controller is used to control the flow, which cannot provide an optimal control due to its inherent disadvantages. To overcome this problem artificial intelligence techniques using fuzzy logic and genetic algorithms have been implemented. The transient and steady state response and performance criteria IAE, ITAE have been discussed. The result shows the better optimization of fuzzy GA controllers.

IMPROVED ALGORITHM AND REALIZATION OF MECHANICAL MULTI-PARAMETER FUZZY OPTIMIZATION

Aiming at the problem of low efficiency and local optimum caused by traditional multi-objective fuzzy optimal methods, the multi-parameter fuzzy optimal mathematical models with improved symmetry and improved asymmetry genetic algorithm based on the overall optimal view are given by considering optimal membership of objectives and constraint. The best optimal solution and optimal constraint value of sub-objective function in the feasible field are found by the mathematical model. The example for helical gear with objective and constraint sets is demonstrated. The algorithm of multi-objective fuzzy optimization is realized by applying the Matlab optimum toolbox. Matlab program to realize multi-parameter fuzzy optimal GA of the helical gear is developed, the best optimal solutions of several fuzzy optimal models is obtained, and the penalty function is used to solve the problem of nonlinear constraint. The result shows that improved fuzzy GA can get satisfying results both in the computing speed and the quality of solution. The method can reflect fuzziness of the problems and practicability of the structure of the engineering project.

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