Abstract-An Evolutionary Programming (EP) and Efficient Particle Swarm Optimization (EPSO) techniques are employed to solve Economic Dispatch (ED) problems including transmission losses in power system is presented in this paper. This paper is clearly justified with the results separately obtained for the above two techniques and also provided with the results by applying both the algorithms separately. With practical consideration, ED will have non-smooth cost functions with equality and inequality constraints that make the problem, a large-scale highly constrained nonlinear optimization problem. The proposed method expands the original PSO to handle a different approach for satisfying those constraints. In this paper, an Efficient Particle Swarm Optimization (EPSO) technique is employed so that optimized results are obtained, and by applying EP, faster convergence is obtained . To demonstrate the effectiveness of the proposed method it is being applied to test ED problems, one with smooth and other with non smooth cost functions considering valve-point loading effects. Comparison with other optimization and hybrid algorithm techniques showed the superiority of the proposed EP-EPSO approach and confirmed its potential for solving nonlinear economic load dispatch problems with losses.

Economic dispatch (ED) is one of the most important optimization problems in a power system. The objective of ED is sharing the power demand among the online generators while keeping the minimum cost of generation as a constraint. The aim of this paper is to operate an electric power system as economically as possible within its security limits. This paper proposes the following 2 new particle swarm optimization (PSO) algorithms to solve a nonconvex economic dispatch problem: an efficient PSO is termed as efficient particle swarm optimization (EPSO), and a hybrid of evolutionary programming (EP) and EPSO is termed as EP-EPSO.Since ED was introduced, several methods have been used to solve these problems. However, none of these methods can provide an optimal solution because they become trapped at some local optima. Stochastic optimization techniques such as EPSO and EP have the advantage of a good convergent property. A significant speed-up can be obtained by the hybrid of these algorithms. The proposed techniques are tested on standard test systems available in the literature. The performance of the proposed EP-EPSO is compared with a) biogeography-based optimization, b) adaptive particle swarm optimization, c) the genetic algorithm, d) a 2-phase neural network, e) PSO with time-varying acceleration coefficients, f ) NEW-PSO, and g) differential evolution with biogeography-based optimization. It is observed that the EP-EPSO has a higher convergence rate, advanced quality, and better optimal cost when compared to the other techniques. The considered ED problems have been solved, including transmission losses without valve-point loading effects.

Ultrasonic frequency vibration coupled micro-wire electrical discharge machining (UFV-[Formula: see text] WEDM) has received enormous consideration due to its zero-tolerance machining. Nickel chromium (Ni–Cr) space alloys are a natural choice within the aerospace industry, which are exposed to high temperatures and high pressure, such as turbine seals and exhaust liners. This study reveals the impact of the UFV-[Formula: see text] WEDM influencing machining parameters like ultrasonic frequency vibration (UFV), servo voltage ([Formula: see text]), time on ([Formula: see text]), cutting angle ([Formula: see text]), time off ([Formula: see text]), and current (I) on the Ni–Cr space alloy in terms of minimum surface undulation (Ra) with maximum material removal rate ([Formula: see text]). The cutting trials are carried out by central composite design (CCD). Analysis of variance (ANOVA) is used to find out the proportionate contribution of several factors, and it discloses that [Formula: see text] was the significant parameter impacting Ra (64.57%) and [Formula: see text] (61.86%). The performance sequence of significant influencing parameters is [Formula: see text]. According to desirability analysis (DA), optimum parameters for numerous solutions are [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text]V, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text]A, and [Formula: see text]. The optimum conditions lead to the highest [Formula: see text] (5.72[Formula: see text]mm3/min) and the lowest Ra (3.42[Formula: see text][Formula: see text]m). Scanning electron, 3D topography, and atomic force microscope images are used to analyze the machined surface.

Abstract-A Hybrid NEURO-EPSO algorithm for solving Economic Load Dispatch Problem (ELD) with non-smooth cost function is presented in this paper. The ELD problem is a highly constrained, large scale, non-linear optimization problem, considering the equality and the inequality constraints. To demonstrate the effectiveness of the proposed algorithm, it is applied to test ELD problem with non-smooth cost function while considering the Valve-Point loading effect. Here we employ an Efficient PSO technique and the Back Propagation Algorithm(NN) to solve the ELD problem, so that faster convergence and more optimized results are obtained compared to other optimization techniques.

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