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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]

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 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]

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]

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 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]