Optimal Power Flow Solution of the Algerian Electrical Network using Differential Evolution Algorithm

Abstract

AbstrakMakalah ini menyajikan algoritma evolusi diferensial (DE)

A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

“…Metaheuristics are stochastic algorithms for solving a wide range of problems for which there is no known effective conventional methods. These techniques are often inspired from biology (Evolutionary algorithms [6][7][8][9][10][11], Differential evolution [12][13][14][15]), physics (Simulated annealing [16][17][18], Gravitational search algorithm [19]) and ethnology [20][21][22][23][24].…”

Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System

“…The results validate the proposed method and prove their performance in terms of solution quality. [30] 1703.8 ---Artificial bee colony FGA [13] 1768.5 ---Fuzzy Genetic Algorithm PGA [31] 1769.7 0.4213 1765.7 0.4723 Decomposed Parallel GA FSLP [32] 1775.856 0.4329 1786 0.4746 Fast successive linear programming ACO [33] 1815.7 ---Ant Colony Optimization GA [33] 1937.1 ---Genetic algorithm BHBO [24] 1710.0859 ---Black-Hole-Based Optimization Statistically: According to the all results obtained through the minimization of treated objectives, I wish to note that the process has run 50 times with different initial solutions for case 1, Table 3 indicates that algorithm offers the minimum values of best, worst, median values of fuel cost, and the average of the average total computational times. We can show that time of proposed MFO method is low, as well as note the difference between the minimum and the worst is very close, from it we can say that the proposed method is robust.…”

“…Therefore optimization is known as one of the most current problem facing research, a good optimization leads to an optimal solution for an efficient system. The first solution method for the OPF problem was proposed by Dommel and Tinney [7] in 1968, and since then numerous other methods have been proposed, some of them are: Ant Colony Optimization (ACO) [8], Genetic Algorithm (GA) [9][10], enhanced genetic algorithm (EGA) [11][12], Hybrid Genetic Algorithm (HGA) [13], artificial neural network (ANN) [14], Particle swarm optimization (PSO) [15], fuzzy based hybrid particle swarm optimization (fuzzy HPSO) [16], Tabu Search (TS) [17], Gravitational Search Algorithm (GSA) [18]. Biogeography based optimization algorithm (BBO) [19], harmony search algorithm (HS) [20], krill herd algorithm (KHA) [21], Cuckoo Search (CS) [22], adaptive group search optimization (AGSO) [23], BlackHole-Based Optimization (BHBO) [24].…”

An Improved Adaptive Niche Differential Evolution Algorithm

“…Besides, it has the spatial distribution of different heights and peaks and it is usually used to measure the performance of the search algorithm in processing the optimization problems with many noises. 4 f has a strong oscillation as well as many traps. Due to the oscillation and the many local optimal points surrounding the global optimal point, it can trick and misguide the population search into local optimal points and it is quite deceptive to the algorithm.…”

“…After that, it conducts greedy selection operation on the parent individuals and the test individuals according to the fitness, retains the better individuals and realizes the population evolution. However, DE algorithm has such problems as bad local search ability, low search efficiency in the post evolution phase and premature convergence, which selection methods to use has been a difficulty for DE algorithm all the time so as to retain the excellent individuals and maintain the [4]. Niche technology has been proposed in the niche implementation method based on pre-selection mechanism by Cavichio in the 1970s for the first time.…”