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Most conventional optimization methods contain the problems with constraints having crisp numbers. But in most practical engineering problems, the constraints contain allowable non crisp numbers that make using fuzzy optimum design inevitable. In this article the combination of α-cut fuzzy method with evolutionary algorithms such as Genetic Algorithm (GA) is investigated. Also some schemes to increase the speed by two space structures. The examples show that using fuzzy programming increase the efficiency of RCGA. [ABSTRACT FROM AUTHOR]

The aim of this paper is to design a position controller of a DC motor by selection of a PID parameters using genetic algorithm. The model of a DC motor is considered as a third order system. And this paper compares two kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm, second is the controller design by the Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler & Nichols' method. The proposed method could be applied to the higher order system also. [ABSTRACT FROM AUTHOR]

The motivation for this paper is to develop an approach to optimization of beam design. Under given loading and support conditions, the comparative strength of three (3) common structural shapes was determined. This led to the conclusion that a particular structural shape together with its dimensions will give the optimal solution in beam design in terms of the least cross-sectional area to support the given load, which would then translate to savings in cost and reduction in weight of the structural member. An investigation was also conducted to take into consideration the effect in the dimensions of the structural shapes of uncertainties due to manufacturing limitations and tolerances. This resulted in an assessment of the order of magnitude of this effect on the design variables. In solving the resulting optimization problems, MATLAB's Genetic Algorithm and Direct Search Toolbox was employed. [ABSTRACT FROM AUTHOR]

Since the evaluation of each individual is based on the time-consuming structural analysis, the computational efficiency of truss topology optimization using genetic algorithm is very low. The paper focuses on this challenging problem. It is observed that there are a number of duplicate individuals appearing repetitively in the evolutionary process. Therefore, an individual identification technique is introduced to avoid evaluating the duplicate individuals by the time-consuming structural analysis but by searching the evolutionary history data to save computing time, the computational complexity of this technique is deduced. The results of two truss examples verify that the technique can effectively improve the efficiency of the algorithm. Based on this identification technique, numeric experiments are implemented to study the influence of several factors, i.e., the population size, the max generation, and the scale of problems, on the proportion of duplicate individuals. Results show that the population size has a significant impact on the proportion, and that both the max generation and the scale of problems have little influence. [ABSTRACT FROM AUTHOR]

This paper studies the simultaneous scheduling of landside container handling operations in a container terminal. Issues addressed include scheduling the sequence of loading (unloading) of containers to (from) the vessels from (to) the quayside, assigning trucks to transport containers between quayside and yard side, and scheduling operations of yard cranes in different yard zones. A mathematical model describing the characteristics of the problem is developed. The objective is to minimize the total completion time for handling all the containers under consideration in the terminal. As optimizing the landside container handling operations in a terminal is known to be NP-hard, a new genetic algorithm with the selection process based on the principle of simulated annealing is developed in this paper to solve the problem. Comparison of the respective results obtained by using the proposed genetic algorithm, the canonical genetic algorithm and the simulated annealing algorithm clearly shows that the total completion times obtained by the proposed algorithm are 12%-18% shorter than that obtained by GA and SA, and the computing times of GA-SA are only 50% of that of GA. The proposed genetic algorithm is indeed superior to the other two algorithms. [ABSTRACT FROM AUTHOR]

Effective and efficient scheduling of yard crane operations is essential to guarantee a smooth and fast container flow in a container terminal, thus leading to a high terminal throughput. This paper studies the problem of scheduling yard cranes to perform a given set of loading and unloading jobs with different ready times in a yard zone. In particular, the inter-crane interference between adjacent yard cranes which results in the movement of a yard crane being blocked by adjacent yard cranes is studied. The objective is to minimize the sum of yard crane completing times. Since the scheduling problem is NP-complete, a new hybrid optimization algorithm combining the techniques of genetic algorithm and tabu search method (GA-TS) is proposed to solve the challenging problem. Two new operators, namely the Tabu Search Crossover (TSC) and the Tabu Search Mutation (TSM), are introduced into the proposed algorithm to ensure efficient computation. A set of test problems generated randomly based on real life data is used to evaluate the performance of the proposed algorithm. Computational results clearly indicate that GA-TS can successfully locate cost-effective solutions which are on average 20% better than that located by GA. Indeed, the proposed hybrid algorithm is an effective and efficient means for scheduling yard cranes in computer terminals. [ABSTRACT FROM AUTHOR]

The classification of solved signal features for manufacturing process condition monitoring has been carried out using fuzzy parameters optimization processing. In cases where assumptions in respect of nonlinear behavior cannot be made, the need to describe mathematically, ever increasing complexity become difficult and perhaps infeasible. The optimization possibilities of the fuzzy system parameters using genetic algorithms are studied. An analytical function determines the positions of the output fuzzy sets in each mapping process, that substitute the fuzzy rule base used in conventional approach. We realize case adaptation by adjusting the fuzzy sets parameters. Fuzzy parameters within optimization procedure could be multiobjective. We solve also the system for cutting process simulation, which contains the experimental model and the simulation model based on genetic algorithms. There is developed a genetic algorithm based simulation procedure for the prediction of the cutting forces. These genetic algorithms methodologies are suitable for fuzzy implementation control and for solution of large-scale problems. [ABSTRACT FROM AUTHOR]

Performance results for finding the best genetic algorithm for the complex real problem of optimal machinery equipment operation and predictive maintenance are presented. A genetic algorithm is a stochastic computational model that seeks the optimal solution to an objective function. A methodology calculation is based on the idea of measuring the increase of fitness and fitness quality evaluation with chaos theory principles applying within genetic algorithm environment. Fuzzy neural networks principles are effectively applied in solved manufacturing problems mostly where multisensor integration, real - timeness, robustness and learning abilities are needed. A modified Mamdani neuro-fuzzy system improves the interpretability of used domain knowledge. [ABSTRACT FROM AUTHOR]

This paper proposes an age artificial immune system (AAIS), for optimal order pickings in an Automated Storage and Retrieval System (AS/RS) with multiple input/ output stations. A mathematical model is presented to describe the characteristics of the AS/RS. It is optimized with the proposed algorithm, which is based on the clonal selection principle and the aging concept. Unlike conventional algorithms for artificial immune systems, the proposed algorithm consists of antibodies whose abilities to be cloned and to survive depend on their ages, and adopts a mutation scheme based on randomized rankings. To further improve the performance of AAIS, a crossover operator is also included in the algorithm to form the AAIS-CX algorithm. The performance of both algorithms is tested with the problems of optimal order pickings in an AS/RS with multiple input/output stations. Comparison of the results obtained by using AAIS-CX, AAIS, the techniques of nearest neighbor heuristics, genetic algorithms and ant colony systems clearly shows that AAIS-CX is superior to the other algorithms. Suggestions for future work are also included [ABSTRACT FROM AUTHOR]

In this paper a new approach to optimize nuclear power plant designs based on global risk reduction are described. In design the focus is on as components quality as redundancy levels. Meanwhile in maintenance and test tasks the focus is on as scheduling tasks as human reliability. The models based on probabilistic risk analysis are used to evaluate several designs and schedules proposed by an hybrid genetic algorithm. The best alternative is chosen to minimize the economical risk of down the production or of have an accident for all reasons considered. This approach has resulted in a new methodology to assure the risk for complex industrial systems too in a global way. So, it is possible considering several aspects such as component qualities, redundancy levels, task schedules for maintenance or tests tasks, and reliability human as a whole. [ABSTRACT FROM AUTHOR]

Applications of the Particle Swarm Optimization (PSO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle swarms is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed PSO is to maximize an objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The feasibility of the proposed method is demonstrated and compared with Genetic Algorithms (GAs) based image enhancement technique. The obtained results indicate that the proposed PSO yields better results in terms of both the maximization of the number of pixels in the edges and the adopted objective evaluation. Computational time is also relatively small in the PSO case compared to the GA case. [ABSTRACT FROM AUTHOR]

Several statistical tools and most recently Functional Networks (FN) have been used to solve nonlinear regression problems. One of the tasks associated with all of these methodologies consists of discovering the functional form of the contribution of the explanatory variables to the response variable. In this paper, we tackle this problem using functional network models (FNs). Since these models usually involve from a moderate to high number of parameters, a genetic algorithm (GA) for model selection is proposed. After an introduction of FNs and GAs, the performance of the proposed methodology is assessed using a simulation study as well as a real-life data set. [ABSTRACT FROM AUTHOR]

This paper presents a detailed comparison between a conventional PI controller and a variable structure controller based on a fuzzy sliding mode strategy used for speed control in direct torque control induction motor drive. Genetic algorithms are used to tune the PI controller gains to ensure optimal performance. The performance of the two controllers are investigated and compared for different dynamic operating conditions such as of reference speed and for load torque step changes at nominal parameters and in the presence of parameter variation and imprecision. Results show that the PI controller has better performance for nominal operating conditions while the fuzzy sliding mode is more robust against parameter variation and uncertainty, and is less sensitive to external load torque disturbances with a fast dynamic response. [ABSTRACT FROM AUTHOR]

In this paper, hybrid particle swarm optimization (PSO) is proposed for solving the challenging multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problem. The objective of this nonlinear optimization problem is to minimize the total fuel cost of the system and at the same time fulfil the transient stability requirements. The optimal power flow (OPF) with transient stability constraints considered is re-formulated as an extended OPF with additional rotor angle inequality constraints, which is suitable for hybrid PSO to solve. Comparison between various existing hybrid PSO techniques is carried out by solving the New England 39-bus system. Experimental results indicate that the hybrid PSO integrated with the mutation operation of genetic algorithms is better than the other existing hybrid PSO methods in both solution quality and stability. [ABSTRACT FROM AUTHOR]

The increased usage of embedded pre-designed reusable cores necessitates a core-based test strategy in which cores are tested as separate entities. Test application time is a major issue in System-on-Chip Testing (SOC). Pre-designed cores and reusable modules are popularly used in the design of large and complex systems. As the complexity of system increases, the test application time also significantly increases. Available techniques for testing of core-based SOC do not provide a systematic means of compact test solutions. The test application time must be minimized to transport test data to and from the cores. In this paper, we present a Genetic Algorithm (GA)-based approach to optimize the test vectors for globally asynchronous locally synchronous SOC Benchmark Circuits. This approach provides optimal results comparable to other methods of similar problems. Based on our experiments, the test results for four ITC-02 SOC Test Benchmark circuits are presented. The results of GA-based approach are shown to be superior to the heuristic approaches proposed in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a multi agent and fuzzy logic based DSS for stock market. This system will help investors of the stock market to take the correct buy/sell/hold decisions. The results obtained from the proposed fuzzy logic model were satisfactory but not accurate. A fuzzy tuning methodology was introduced to enhance the accuracy of the decisions. The tuning methodology which uses genetic algorithms is presented also in this paper. A multi agent framework is proposed for the implementation of the system. Experimental simulation using actual price data form NASDAQ index is carried out to demonstrate the power of the proposed model. [ABSTRACT FROM AUTHOR]

This paper presents a combined model approach of Fuzzy Logic and Genetic Algorithm applied for non-linear behavioral compensation of Silicon Controlled Rectifier (SCR), for its improved performance (optimal variable output voltage). The optimized parametric compensation of SCR will be done by amalgamated algorithm of Fuzzy Logic Control and Genetic Algorithm. It is a shift from existing practice of Fuzzy Logic based control /compensation, as reported in the literature. In this work, a Fuzzy Logic based optimal control system has been developed for input voltage regulation of SCR, which is further optimized by Genetic Algorithm. The input voltage regulation of SCR is needed to meet the varying load current demand in various industrial applications of the device. The proposed scheme as presented in this paper leads to the optimal regulation of input voltage for SCR. The results have shown a remarkable reduction in the error which was otherwise existing in the device and its application circuit. The accuracy level at the output of the SCR after the implementation of the proposed amalgamated algorithm is ranging between 99.0 to 99.5%. It also suits the nonlinearly varying load current requirement for a given industrial system employing SCR. [ABSTRACT FROM AUTHOR]

In this paper, we present an interesting filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of model based body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In order to deal with this problem, the IMM algorithm was combined with the Unscented Kalman Filter (UKF) [6]. Even if the later algorithm proved its efficacy in nonlinear model case; it presents a serious drawback in case of non Gaussian noise. To deal with this problem we propose to substitute the UKF with the Particle Filter (PF). To overcome the problem of data association, we propose the use of the JPDA approach, [12]. To reduce the computational burden of the latter technique, we choose firstly the most likely feasible events by applying a Genetic Algorithm; finally the derived algorithm from the combination of the IMM-PF algorithm and the GA-JPDA approach is noted GA-JPDA-IMM-PF. [ABSTRACT FROM AUTHOR]

The genetic programming approach is applied to the problem of aircraft autolanding, subject to wind disturbances. The derived control law is tested successfully, using a linearised model of a commercial aircraft. The evolutionary control of autolanding is done within the desired operational envelope. [ABSTRACT FROM AUTHOR]

In this paper we used genetic algorithms to 1 find the solution of game theory. We proposed new method foe solving game theory and find the optimal strategy for player A or player B. We can benefit from the relationship between game theory and the linear programming to find the fitness function and tested this fitness function at different examples . [ABSTRACT FROM AUTHOR]