Fuzzy Adaptive Genetic Algorithm Research Articles

Multi-Objective Path Optimization of Highway-Railway Multimodal Transport Considering Carbon Emissions

With the increase in carbon emissions from railway transit, green transportation has attracted worldwide attention due to its low pollution and low consumption. In order to improve the transportation efficiency of multimodal transport and reduce carbon emissions, this paper makes a systematic study on the comprehensive optimization model and method of multiple transport tasks and transport modes considering carbon emissions. Firstly, an optimization model is established with transportation distance, transportation time, and carbon emission as transportation objectives. Secondly, an improved fuzzy adaptive genetic algorithm is designed to adaptively select crossover and mutation probabilities to optimize the path and transportation mode by using population variance. Finally, an example is designed, and the method proposed in this paper is compared with the ordinary genetic algorithm and adaptive genetic algorithm, which proves the proposed model and algorithm are effective. In conclusion, it is found that the present multi-objective optimization model based on the improved genetic algorithm can adjust multimodal transport plans and reduce carbon dioxide emissions, which provides a reference basis for logistics enterprises to carry out multimodal transport.

A Workflow Scheduling Approach With Modified Fuzzy Adaptive Genetic Algorithm in IaaS Clouds

The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms.

A two-level optimization algorithm for path planning of bionic robotic fish in the three-dimensional environment with ocean currents and moving obstacles

Path planning is not only the premise of underwater navigation and obstacle avoidance, but also the core and key to improve the operation durability for the bionic robotic fish. In this paper, the path length, smoothness and safety factor are selected as the optimization performance indexes. In order to solve the path planning with ocean currents and moving obstacles, a two-level optimization algorithm is proposed for the bionic robotic fish. In the first level of optimization, it solves the global path planning problem with known environment. In the second level of optimization, based on the optimization results of the first level of optimization, the local optimal path is obtained by the dynamic optimization method in the environment with unknown static and moving obstacles, which can solve the problem of obstacle avoidance for irregular obstacles effectively. The two-level optimization algorithm improves the accuracy of the path planning. Fuzzy adaptive genetic algorithm (FAGA) is given to further improve the optimization performance. The proposed algorithm can improve the optimization performance of the path planning, whose effectiveness is demonstrated by the simulation and experiment results.

Effective Power Congestion Management Technique Using Hybrid Nelder–Mead – Grey Wolf Optimizer (HNMGWO) in Deregulated Power System

The aim of this article is to apply Hybrid Nelder–Mead – Grey Wolf Optimizer (HNMGWO) in congestion management problem with optimal congestion cost by rescheduling generators in the power system. When the transmission power lines are overloaded in the power system, the following strategies are followed: generator rescheduling and installation of FACTS devices on the lines and load curtailment. But, load curtailment is not performed as the deregulated system encourages customer satisfaction. Thus, generator rescheduling is selected for problem solution as it does not involve the creation of any new infrastructure. Grey Wolf Optimizer (GWO) is one of the recent optimization techniques which work based on the leadership hierarchy and the hunting technique of grey wolves. The effective local search is performed by the Nelder–Mead (NM) method and the output is used to initialize the population for GWO, which searches global best value. In this paper, the HNMGWO is attempted based on the positive outcome of both NM and GWO. This proposed algorithm has been applied and results have been discussed related to the tasks of Independent System Operator. The proposed HNMGWO is applied in a standard IEEE 30 bus and IEEE 118 bus systems and thereby results are compared with standard GWO, Particle Swarm Optimization (PSO), fuzzy adaptive PSO, genetic algorithm, and bacterial foraging algorithms. The result shows that the proposed work is more effective by consuming minimum congestion cost in reduced rescheduled power and power loss.

Adaptive fuzzy genetic algorithm for multi biometric authentication

Biometric Authentication (BA) has turn out to be presently as key problem in privacy and security. Multimodal biometric system specializes in enhancing verification overall performance of the users for authentication. On this direction, biometrics which is the computer-based validation of an individuals’ identification is turning into increasingly more vital, particularly for high security systems. The spirit of biometrics is the size of character’s behavioral or physiological characteristics; it allows authentication of someone’s identity. Biometric-based totally authentication is likewise turning into increasingly more essential in computer based applications because the quantity of touchy records saved in such structures is growing. The latest needs of biometric systems are robustness, high reputation quotes, capability to handle imprecision, uncertainties of non-statistical kind and magnanimous flexibility. Its miles precisely right here that, the role of soft computing techniques involves vital play. The primary aim of this write-up is to offer a practical view on applications of soft computing strategies in biometrics and to analyze its impact. It is found that soft computing has already made inroads in phrases of man or woman techniques or in combination. This paper additionally proposes as hybrid soft computing based optimization device named “Adaptive Fuzzy Genetic Algorithm (AFGA)” which is adaptable to all of the unimodal and multimodal biometric authentication system. The results acquired by means of this device insure high standard of verification via multi-modal biometrics fusion by means of powerful functionality of fuzzy logic. Experimental investigation under various biometric data conditions exhibits notable effects over current strategies.

Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems

AbstractIn the industrial and manufacturing fields, many problems require tuning of the parameters of complex models by means of exploitation of empirical data. In some cases, the use of analytical methods for the determination of such parameters is not applicable; thus, heuristic methods are employed. One of the main disadvantages of these approaches is the risk of converging to “suboptimal” solutions. In this article, the use of a novel type of genetic algorithm is proposed to overcome this drawback. This approach exploits a fuzzy inference system that controls the search strategies of genetic algorithm on the basis of the real-time status of the optimization process. In this article, this method is tested on classical optimization problems and on three industrial applications that put into evidence the improvement of the capability of avoiding the local minima and the acceleration of the search process.

Design and development of 3-stage determination of damage location using Mamdani-adaptive genetic-Sugeno model

Damage detection in structural elements like beams is one of important research areas for health monitoring. Initiation of a fault in the form of a crack or any damage puts a limitation on the service life of a structural member. So, in this paper, a method is proposed which uses the advantages of soft computing techniques like Fuzzy Inference Systems (Mamdani and Sugeno) and Adaptive Genetic Algorithm for three stage refinement of the data base generated using dynamic responses from a cracked fixed-free aluminum alloy beam element. For the crack element reference, a finite element model of a single transverse crack has been considered. The proposed method describes both Mamdani and Sugeno Fuzzy Inference Systems for training of damage parameters. In the Adaptive Genetic Algorithm, a statistics based method has been incorporated to limit the randomness of the search process. Finally, the results from the Mamdani-Adaptive Genetic-Sugeno model (MAS) are validated with the results from the experimental analysis.

CONCEPTUAL COST ESTIMATIONS USING NEURO-FUZZY AND MULTI-FACTOR EVALUATION METHODS FOR BUILDING PROJECTS

During the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, (2) fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (3) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new pro­ject. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thor­oughly respond to the project characteristics, the proposed model has a high probability of increasing estimation accura­cies more than the three conventional methods, i.e., average unit cost, component ratios, and linear regression methods.

Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems

In the industrial and manufacturing field, many problems require the tuning of the parameters of complex empirical or theoretical models by means of the exploitation of data. In some cases the use of analytical methods for the determination of such parameters is not applicable, thus heuristic methods are employed. One of the main disadvantages of the use of these latter approaches is the risk of converging to a sub-optimal solution due to characteristics of the search surface which is determined both by the model and the data available for the tuning. In this paper the use of a novel type of genetic algorithms is proposed to overcome this drawback. This approach exploits a fuzzy inference system that controls the search strategies of the genetic algorithm on the basis of the real-time status of the optimization process. In this paper this method is tested on three problems drawn from the steel-making industry that put into evidence the improvement of the capability of avoiding the local minima of the optimization problem and the acceleration of the search process.

Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems

This paper aims at multi-objective straight and U-shaped assembly line balancing problems with the fuzzy task processing times. The problems are referred to herein as f-SALBP and f-SULBP and the objectives that are considered to be satisfied are: (a) minimizing the numbers of stations, (b) maximizing the fuzzy line efficiency, (c) minimizing the fuzzy idleness percentage, and (d) minimizing the fuzzy smoothness index. In fact, the f-SALBP and f-SULBP are SALBP and SULBP generalization in fuzzy circumstance, respectively. Initially, the two problems are formulated and due to the uncertainty, variability and imprecision that often occurred in real-world production systems, the processing time of tasks are supposed as triangular fuzzy numbers. Then, to solve the problem, a hybrid multi-objective genetic algorithm is proposed. A One-Fifth Success Rule (OFSR) is deployed for the selection and mutation operators to improve the genetic algorithm's performance. The results in the genetic algorithm are being controlled in convergence and diversity simultaneously by means of controlling the selective pressure (SP) and mutation rate. Likewise, a fuzzy controller to SP is employed for the OFSR toward a better implementation of the genetic algorithm. In addition, the Taguchi design of experiments is used for parameter control and calibration. Finally, the numerical examples are presented to compare the performance of the proposed method with the existing ones. The results show significantly better performance for the proposed algorithm.

Fuzzy Position Control of PMBLDC Motor using Adaptive Genetic Algorithm

In this paper, an identification of position controller design for Permanent Magnet Brushless DC Motor (PMBLDCM) using Fuzzy Adaptive Genetic Algorithm (FAGA) is presented. For optimal searching of the scaling factors of the antecedent and consequent parameters of the T-S fuzzy model is identified to minimize the Integral square error (ISE) between the closed-loop outputs of the motor system and the Fuzzy T-S model. An interesting alternative that could be investigated the use of fuzzy logic control methods.The mechanism of designing and implementation of transfer system (T.S).model FLC based on AGA is described and the simulation results are obtained using the proposed technique in comparison with the conventional PI controller is prescribed. The simulation result shows that the identification using the Adaptive genetic Algorithm based Fuzzy transfer system (T.S) model has smaller error when compared to the transfer function (T.F) model.Keywords: Adaptive Genetic Algorithm, Fuzzy Adaptive Genetic Algorithm, Integral Square Error, Permanent Magnet Brushless DC Motor, Position Controller

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.

Hybrid Method for Vehicle Detection from CCTV Captured Image

This paper presents a hybrid method for vehicle detection from CCTV captured image. In order to overwhelm such complex details of the color image, the system combines artificial intelligence techniques to achieve automatic vehicle detection. These are techniques 2D principal component analysis (2DPCA), Fuzzy adaptive resonance theory (Fuzzy ART), genetic algorithm (GA) and self-organizing map. The proposed system can detect different vehicle sizes from different proportional image area. Bilinear interpolation is used to resize each proportional image area to vehicle feature matrix. The proposed system can detect various types of vehicles from the difference image background.

Optimal Design of Photovoltaic–Battery Systems Using Interval Type-2 Fuzzy Adaptive Genetic Algorithm

Many countries have been triggered to provide a new energy policy which promotes renewable energy applications because of public awareness to reduce the global warming and rising in fuel prices. Renewable energy sources such as solar energy are green and promising energy in the future for widespread use. Combining renewable energy sources with battery makes electricity supply more economical and reliable to meet all possible load level. This paper proposed a new hybrid method to optimize Photovoltaic (PV)-Battery systems. The proposed method was named Interval type-2 fuzzy adaptive genetic algorithm (IT2FAGA). Genetic algorithm (GA) is one of modern optimization techniques that has been successfully applied in various areas of power systems. To enhance the ability of GA to prevent trapping in local optima and increase convergence in a global optima, the crossover probability (pcross) and the mutation probability (pmut), parameters in GA, are tuned using interval type-2 fuzzy logic (IT2FL). Objective function used in this paper was the annual cost of sytem (ACS) consisting of the annual capital cost (ACC), annual replacement cost (ARC), annual operation cost maintenance (AOM). The proposed method was also compared to fuzzy adaptive genetic algorithm (FGA) and standard genetic algorithm (SGA). Simulation results indicated that the proposed<

A hybrid of adaptive neuro-fuzzy inference system and genetic algorithm

Premature convergence is an important problem in evolutionary algorithms, in particular genetic algorithm. The diversity of the population is a very influence paprameter on premature convergence in genetic algorithm. In this paper, we attempt to improve the performance of genetic algorithms by providing a bi-linear allocation lifetime approach to label the chromosomes based on their fitness values. These labales applied within a set of fuzzy rules and adaptive neuro-fuzzy inference system genetic algorithm to select suitable sexual chromosomes for recombination. We have evaluated the proposed technique on several numerical functions by comparing its performance to the basic genetic algorithm. The results of our initial experiments demonstrate a clear advantage of the adaptive neuro-fuzzy inference system genetic algorithm over the other techniques.

An Improved Fuzzy Adaptive Genetic Algorithm for Function Optimization

The critical operators for genetic algorithms to avoid premature and improve globe convergence is the adaptive selection of crossover probability and mutation probability. This paper proposed an improved fuzzy adaptive genetic algorithm in which the variance of population and individual fitness value are used to measure the overall population diversity and individual difference, meanwhile, both of them are applied to design fuzzy reference system for adaptively estimation of crossover probability and mutation probability. Simulation results of function optimization show that the new algorithm can converge faster and is more effective at avoiding premature convergence in comparison with standard genetic algorithm.

A SOLUTION TO AN ECONOMIC DISPATCH PROBLEM BY A FUZZY ADAPTIVE GENETIC ALGORITHM

In practice, obtaining the global optimum for the economic dis- patch (ED) problem with ramp rate limits and prohibited operating zones is presents diculties. This paper presents a new and ecient method for solving the economic dispatch problem with non-smooth cost functions using a Fuzzy Adaptive Genetic Algorithm (FAGA). The proposed algorithm deals with the issue of controlling the exploration and exploitation capabilities of a heuristic search algorithm in which the real version of Genetic Algorithm (RGA) is equipped with a Fuzzy Logic Controller (FLC) which can eciently explore and exploit optimum solutions. To validate the results obtained by the proposed FAGA, it is compared with a Real Genetic Algorithm (RGA). Moreover, the results obtained by FAGA and RGA are also compared with those obtained by other approaches reported in the literature. It was observed that the FAGA outperforms the other methods in solving the power system economic load dispatch problem in terms of quality, as well as convergence and success rates.

Fuzzy adaptive genetic algorithm based on auto-regulating fuzzy rules

There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms (SGA) as the values of crossover probability (Pc) and mutation probability (Pm) are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm (FAGA) based on the auto-regulating fuzzy rules (ARFR-FAGA). By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise (EFAGA) obviously in the global search.