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This paper presents a comparison of metaheuristics applied to the power system interdiction problem. This problem considers the interaction of a disruptive agent and a system operator. The disruptive agent aims at maximizing the damage of the power system, measured as load shedding. This agent has limited resources and must select the combination of elements to attack that result in the greatest damage to the system, anticipating the reaction of the system operator. On the other hand, the system operator must minimize the load shedding by redispatching generation resources. The interdiction model is nonlinear, nonconvex and multimodal. The solutions provided by three metaheuristic techniques are compared: Genetic Algorithm, GRASP and Iterated Local Search. Results show that the Iterated Local Search adapts better to the type of problem and allows obtaining the best rate between quality of solutions and computation time. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology based on decentralized operation and coordination of large power systems, obtaining the system's optimization in a simplified way, and reducing time consumption. The optimal search of generator voltage, tap changer and compensation, is accomplished by using an evolutionary technique, and considering the problem as a multi-objective optimization case. In this paper, both the decomposition network and the optimization problem are applied to two study cases (39 and 190 buses). Global optimal operation is attained, preserving the voltage stability and the exchanges among areas, with minimal operational information, simplicity in the equivalents and time consuming reduction. Results are similar to those obtained by a centralized optimization strategy. [ABSTRACT FROM AUTHOR]

In this paper an innovative focus to programming Job Shop systems, based on sustainability, is presented. The following sustainability variables are evaluated: carbon dioxide emissions (environmental variable), sound intensity, accident rate and handled weight (social variables: occupational health) and finally, makespan time and direct labor costs (economic variables). The methodology is structured in fourteen steps that cover aspects such as multiobjective method design, description of genetic algorithms, and comparison with other methods. It is determined that the designed method shows greater performance for the variable weighted factor P(Xj) when they are compared with results from other methods such as programming rules and mobile bottle neck method. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of optimal location and sizing of distributed generation in distribution systems. For such an assessment the following search techniques were implemented: i) simulated annealing, ii) variable neighborhood search, iii) genetic algorithm and iv) hybrid genetic algorithm. Two objectives have been considered in the optimal location and sizing problem: the minimization of electric power losses and the improvement of voltage profile. To compare the effectiveness of the methodologies a 34 bus distribution test system is used. The correct location and sizing of the distributed generation allowed substantially improving the voltage profile of the network and reducing in 80% the power losses in the test system. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for optimal Distributed Generation (DG) allocation using a hybrid genetic algorithm. In this case, the objective function is the minimization of power losses. The proposed algorithm involves an artificial neural network to evaluate the fitness function and a local search subroutine that allows the algorithm to explore a wider search space, and eventually, escape from local optimal solutions. The proposed approach was tested on a 34-bus system showing that the proper allocation of DG units allows the reduction of active power losses and the improvement of voltage profile. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the variables: makespan time, defective fraction due to operator fatigue and direct labor cost, in a job shop manufacturing system is presented. Two methods are compared; the first one blends elements from VEGA and MOGA algorithms (V&M), while the second one corresponds to a weighted sums method (WSM). Applying these methodologies, it was found that the method V&M has a better performance than the WSM. Comparing he variable makespan time (in hours) the first is better by 7.0 %; comparing the variable makespan time (in weeks) by 6.7 %; and comparing the variable defective fraction by 1.4 %. Regarding the variable labor costs, both methods have the same performance. [ABSTRACT FROM AUTHOR]

This paper describes a solution method to the problem of processing n jobs on m non-related parallel machines. It is a linear and combinatorial generalized allocation problem that considered a sequencedependent setup time and dynamic job entry. A genetic algorithm with integer coding and random generation of population, parent selection, crossover and mutation is proposed. There are two descendants per generation that are compared against the worst existing element to enter to population. After a number of generations that is proportional to the product of nxm the solution is generated. The jobs are sequenced on each machine by due date and computational times are acceptable. It is concluded that the proposed genetic algorithm is an effective and efficient solution that focuses on reducing processing time and on meeting deadlines. [ABSTRACT FROM AUTHOR]

This paper proposes the implementation of an optimum academic agenda to solve the problem of scheduling a school day in Colombia. This is done by taking into account not only the appropriate assignment of rooms and professors but considering the cognitive rhythms of the students as the most relevant factor in the optimization. The Non-dominated Sorting Genetic Algorithm, NSGA-II is proposed as a solution method. The results show a higher efficiency of the algorithm when compared to other algorithms applied to the same problem and evaluated in the same form. At the experimental level the methodology shows a reduction in academic mortality rates in the groups where the methodology was implemented compared to groups that did not considered the cognitive rhythms of the students. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective analysis including variables of the environment of the energy system which is conditioned by the use of solid fuels, for this reason the analysis includes the external characteristics of the resource (fuel) of residue (ash) and the product (electricity/steam). The energy system used is Termocesar, a Colombian process for generating electricity from carbon. Three functions named thermoeconomic objective function, technology objective function and environmental objective function, are analyzed to establish the best location of the energy The genetic algorithm method is used for the optimization of the system. The results were analyzed for nine combinations of weights and show that for most of the cases the optimum includes coals from the Department of Cesar in Colombia. [ABSTRACT FROM AUTHOR]

In this paper the finite element method is applied to the analysis of a three-phase magnetic induction motor squirrel-cage. Different geometries have been applied in the design of the rotor. These geometric variations of the rotor create an electromagnetic profile for each type of geometry. To study the rotor motion, a frequency analysis of the model has been done. The identifying parameters deduced from the finite element method, are adjusted by evolutionary genetic algorithms. Thus, the parameters in the circuit model were identified for each geometry under consideration. A check of the torque was done to validate the model, verifying the accuracy of the method. [ABSTRACT FROM AUTHOR]

This paper presents the application of an evolutionary algorithm using Matlab to obtain the optimum main angles of the cutting geometry of a machining tool. The optimization method considered the non linear model of Kaldor and Venuvinod. Different algorithm configurations were tested using data obtained from the literature. Several simulations were carried out to validate the performance of the algorithm and to show the usefulness of the method employed. It is concluded that that the proposed approach shows to be stable and coherent in the results for the cases studied. [ABSTRACT FROM AUTHOR]