An Improved Differential Evolution and Its Industrial Application

Abstract

In this paper, an improved Differential Evolution (DE) that incorporates double wavelet-based operations is proposed to solve the Economic Load Dispatch (ELD) problem. The double wavelet mutations are applied in order to enhance DE in exploring the solution space more effectively for better solution quality and stability. The first stage of wavelet operation is embedded in the DE mutation operation, in which the scaling factor is governed by a wavelet function. In the second stage, a wavelet-based mutation operation is embedded in the DE crossover operation. The trial population vectors are modified by the wavelet function. A suite of benchmark test functions is employed to evaluate the performance of the proposed DE in different problems. The result shows empirically that the proposed method out-performs signifycantly the conventional methods in terms of convergence speed, solution quality and solution stability. Then the proposed method is applied to the Economic Load Dispatch with Valve-Point Loading (ELD-VPL) problem, which is a process to share the power demand among the online generators in a power system for minimum fuel cost. Two different conditions of the ELD problem have been tested in this paper. It is observed that the proposed method gives satisfactory optimal costs when compared with the other techniques in the literature

Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

“…As a result, a larger value of | | at the early stage of evolution gives a larger searching space for the solution; when | | is small at the later stage of evolution, the algorithm gives a smaller searching space for fine-tuning. The details on determining the value of a are discussed in [25]. After the operations of the wavelet mutation, the population is updated by comparing each trial vectorū i,g to the corresponding target vector x i,g using the method of standard DE as given by (13).…”

“…4 shows the pseudo code of WM-DE. The details of the performance and comparison after using wavelet mutation in DE were reported in [25].…”

“…The result shows that DWM-DE performs well in the training process. The details of the performance and comparison after using the wavelet mutation in DE were reported in [25]. However, the performance of DWM-DE still depends quite much on some control parameters' values and the setting of the initial conditions.…”

Non-Invasive Nocturnal Hypoglycemia Detection for Insulin-Dependent Diabetes Mellitus Using Genetic Fuzzy Logic Method

“…39 To optimize the fuzzy rules and fuzzy membership functions of the FIS, the global searching algorithm of genetic algorithm (GA) 40 is investigated. The traditional optimization methods like the least square algorithm and gradient descent methods have the potential problem of trapping in local optima, while evolutionary algorithms such as particle swarm optimization (PSO), 41 GA, 40 di®erential evolution (DE), 42 and ant colony optimization 43 can be considered. They are e±cient global optimization algorithms particularly good for handling multimodal problems.…”