The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing

This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. The proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.

PERAMALAN MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK DENGAN ALGORITMA ADAPTIVE SIMULATED ANNEALING (Studi kasus: Harga minyak mentah dunia yang dipublikasikan oleh OPEC)

Today, crude oil trading industry is still an important industry in the world because it still has high fuel oil consumption. The crude oil prices tend to fluctuate causing the prediction of crude oil in the coming periods to be a challenge. Forecasting the price of crude oil can be done by various methods, one of them is ARIMA Box-Jenkins model with OLS method to estimate the parameter, but this method has several assumptions that must be met. As time goes by, many methods that discovered, one of them is artificial neural network which can combined with various parameter optimization methods such as Adaptive Simulated Annealing algorithm. Adaptive Simulated Annealing algorithm is an optimization method that inspired by the process of crystallization, the advantages of this algorithm has a running time faster than similar algorithms. The combination of artificial neural networks and Adaptive Simulated Annealing algorithms can be used to model the historical data without requiring assumptions in the analysis. Based on the analysis on this research, the best model is obtained FFNN 2-5-1 with MAPE value of 1.0042%. Keywords: neural network, Adaptive Simulated Annealing, crude oil.

Optimization Techniques for the Economic Dispatch Problem in Various Generation Plant

The dynamic economic dispatch (DED) occupies important place in a power systems operation and control. It aims to determine the optimal power outputs of on-line generating units in order to meet the load demand and reducing the fuel cost. The nonlinear and non convex characteristics are more common in the DED problem. Therefore, obtaining a optimal solution presents a challenge. In the proposed system, firefly algorithm, Adaptive simulated annealing algorithm, artificial bee colony (ABC) algorithm a recently introduced population-based technique is utilized to solve the DED problem. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters. Keywords: ABC-Artificial Bee Colony Algorithm, DED-Dynamic Economic Dispatch, FA-firefly algorithm, ASA-Adaptive Simulated annealing algorithm