In this paper, we apply a mutation operation based on a multivariate Cauchy distribution to fast evolutionary programming and analyze its effect in terms of various function optimizations. The conventional fast evolutionary programming in-cooperates the univariate Cauchy mutation in order to overcome the slow convergence rate of the canonical Gaussian mutation. For a mutation of n variables, while the conventional method utilizes n independent random variables from a univariate Cauchy distribution, the proposed method adopts n mutually dependent random variables that satisfy a multivariate Cauchy distribution. The multivariate Cauchy distribution naturally has higher probabilities of generating random variables in inter-variable regions than the univariate Cauchy distribution due to the mutual dependence among variables. This implies that the multivariate Cauchy random variable enhances the search capability especially for a large number of correlated variables, and, as a result, is more appropriate for optimization schemes characterized by interdependence among variables. In this sense, the proposed mutation possesses the advantage of both the univariate Cauchy and Gaussian mutations. The proposed mutation is tested against various types of real-valued function optimizations. We empirically find that the proposed mutation outperformed the conventional Cauchy and Gaussian mutations in the optimization of functions having correlations among variables, whereas the conventional mutations showed better performance in functions of uncorrelated variables.

This paper applies the Evolutionary Programming (EP) algorithm and a risk assessment technique to obtain an optimal solution to the Unit Maintenance Scheduling Decision (UMSD) problem subject to economic cost and power security constraints. The proposed approach employs a risk assessment model to evaluate the security of the power supply system and uses the EP algorithm to establish the optimal unit maintenance schedule. The effectiveness of the proposed methodology is verified through testing using the IEEE Reliability Test System (RTS). The test results confirm that the proposed approach can to ensure that the system security and outperforms the existing deterministic and stochastic optimization methods both in terms of the quality of the solution and the computational effort required. Therefore, the proposed methodology represents a particular effective technique for the UMSD.

This paper presents a real-time decision support system (RDSS) based on artificial intelligence (AI) for voltage collapse avoidance (VCA) in power supply networks. The RDSS scheme employs a fuzzy hyperrectangular composite neural network (FHRCNN) to carry out voltage risk identification (VRI). In the event that a threat to the security of the power supply network is detected, an evolutionary programming (EP)-based algorithm is triggered to determine the operational settings required to restore the power supply network to a secure condition. The effectiveness of the RDSS methodology is demonstrated through its application to the American Electric Power Provider System (AEP, 30-bus system) under various heavy load conditions and contingency scenarios. In general, the numerical results confirm the ability of the RDSS scheme to minimize the risk of voltage collapse in power supply networks. In other words, RDSS provides Power Provider Enterprises (PPEs) with a viable tool for performing on-line voltage risk assessment and power system security enhancement functions.

This paper presents a multi-thread evolutionary programming (MEP) technique that is composed of global, local, and minimal search units. An appropriate search routine is called depending on the current situation and the individuals are updated by using the selected routine. In each search routine, the individuals are updated with a normalized relative fitness function to improve the robustness of the algorithm. The proposed method is applied to the problem of backing up a truck-and-trailer system to a loading dock. A fuzzy logic controller is designed for a truck-and-trailer backer-upper system and the MEP algorithm is used to optimize the representative parameters of the fuzzy logic controller. The simulation results show that the proposed controller performs well even under a large variety of initial positions.