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Stochastic (or probabilistic) programming (SP) is an optimization technique in which the constraints and/or the objective function of an optimization problem contain random variables. The mathematical models of these problems may follow any particular probability distribution for model coefficients. The objective here is to determine the proper values for model parameters influenced by random events. In this study, two modified differential evolution (DE) algorithms namely, LDE1 and LDE2 are used for solving SP problems. Two models of SP problems are considered; Stochastic Fractional Programming Problems and Multiobjective Stochastic Linear Programming Problems. The numerical results obtained by the LDE algorithms are compared with the results of basic DE, basic particle swarm optimization (PSO) and the available results from where it is observed that the LDE algorithms significantly improve the quality of solution of the considered problem in comparison with the quoted results in the literature. [ABSTRACT FROM AUTHOR]

This article presents particle swarm optimization (PSO)-based optimal gain tuning of proportional integral (PI) speed controller in an induction motor (IM) drive (30 hp) with mine hoist load diagram. Optimization considers the load and speed variations, and provides appropriate gains to the speed controller to obtain good dynamic performance of the motor. IM performance is checked with the optimal gains through the simulation studies in MATLAB/SIMULINK environment. Results are compared with hand tuning (fixed gains) and fuzzy logic (FL) speed controller. Hybrid of FL and PSO-based PI controller for the speed control of given motor is also performed to eliminate the drawbacks of PI controller (overshoot and undershoot) and FL controller (steady-state error). From the simulation studies, hybrid controller produces better performance in terms of rise time, overshoot and settling time. [ABSTRACT FROM PUBLISHER]

Induction motor is the most frequently used electric machine in industrial and commercial applications because of its well-known advantages, including robustness, low price, maintenance free, and line start. The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and its in situ efficiency determination. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. In this present study, particle swarm optimization, differential evolution, and some of their recent variants are used for in situ efficiency determination of induction motor (5 hp) without performing no-load test. Detailed information about the methods for efficiency measurement is given. Results are compared with genetic algorithm and a physical efficiency measurement method, called torque-gauge method. The performances in terms of objective function (error in the efficiency) and convergence time prove the effectiveness of optimization algorithms used in this article. [ABSTRACT FROM AUTHOR]

This paper presents a variant of diversity guided Particle Swarm Optimization (PSO) algorithm named QIPSO for solving global optimization problems. In QIPSO the conventional framework of PSO is modified by including a crossover operator to maintain the level of diversity in the swarm population. Numerical results show that the induction of a crossover operator not only discourages premature convergence to the local optima but also explores promising regions of the search space effectively. Empirical results show the superior performance of QIPSO with conventional PSO and ARPSO. Efficiency of QIPSO is further validated by applying it to a set of five real life problems (RLPs) with constraints. Penalty method is used for dealing with constraints. Once again the simulation results show the compatibility of QIPSO for solving real life problems. [ABSTRACT FROM AUTHOR]