a b s t r a c tIn this paper, an improved algorithm based on Pattern Search method (PS) to solve the Dynamic Economic Dispatch is proposed. The algorithm maintains the essential unit ramp rate constraint, along with all other necessary constraints, not only for the time horizon of operation (24 h), but it preserves these constraints through the transaction period to the next time horizon (next day) in order to avoid the discontinuity of the power system operation. The Dynamic Economic and Emission Dispatch problem (DEED) is also considered. The load balance constraints, operating limits, valve-point loading and network losses are included in the models of both DED and DEED. The numerical results clarify the significance of the improved algorithm and verify its performance.

Abstract-A new algorithm for solving MAX-SAT problems is introduced which clusters good solutions, and restarts the search from the closest feasible solution to the centroid of each cluster. This is shown to be highly efficient for finding good solutions of large MAX-SAT problems. We argue that this success is due to the population learning the large-scale structure of the fitness landscape. Systematic studies of the landscape are presented to support this hypothesis. In addition, a number of other strategies are tested to rule out other possible explanations of the success.Preliminary results are shown indicating that extensions of the proposed algorithm can give similar improvements on other hard optimisation problems.

Hill-climbing has been shown to be more effective than exhaustive search in solving satisfiability problems.Also, it has been used either by itself or in combination with other methods to solve the most difficult region of SAT, the phase transition. We show that hill-climbing also finds SAT problems difficult around the phase transition. It too follows an easyhard-eays transition.

In many image analysis problems it is possible to take advantage of the structural relationships between various parts of the objects being imaged in order to index the images of the objects. For example, many satellites consists of a main body and outlying sub-components. Thus, in many circumstances satellites can be indexed in a model database by the distinct structural relationships between their sub-components. However, algorithms based on structured subcomponents necessitate the use of robust and reliable 2-D image segmentation techniques to successfully partition images into their sub-components. Unfortunately, this segmentation task can be highly problematic for objects with complex components and under harsh, unfavorable lighting conditions.The research presented here describes a new method to compute indices which can be used for image indexing without image segmentation. We use satellite imagery as a convenient image class for which to demonstrate our method. Our method partitions the image into many small equal-area pieces. We refer to this technique as differentiation. Differentiated images result in a set of sub-images that collectively represent the structural information inherent in the image. We prove that a primitive matrix with at most four non-zero eigenvalues can be constructed from the differentiated image. This property 1) significantly reduces storage requirements for a model database, 2) reduces the computational burden of subsequent recognition processes, and 3) supports an efficient and accurate matching procedure. To evaluate the efficiency of our algorithm for a recognition application, we use boundary methods as a feature set evaluation method to quantify the utility of the eigen-indexes obtained by our method as compared to other existing indexing methods.

A major problem associated with vector quantization is the complexity of exhaustive codebook search. This problem has hindered the use of this powerful technique for lossy image compression. An exhaustive codebook search requires that an input vector be compared against each code vector in the codebook in order to find the code vector that yields the minimum distortion. Because an exhaustive search does not capitalize on any underlying structure of the code vectors in hyperspace, other researchers have proposed techniques that exploit codebook structure, but these techniques typically result in sub-optimal distortion.We propose a new method that exploits the nearest neighbor structure of code vectors and significantly reduces the number of computations required in the search process. However, this technique does not introduce additional distortion, and is thus optimal.Our method requires a one time precomputation and a small increase in the memory required to store the codebook. In the best case, arising when the code vectors are largely dispersed in the hyperspace, our method requires only partial search of the codewords. In the worst case, our method requires a full search of the codebook. Since the method depends on the structure of the code vectors in the hyperspace, it is difficult to determine its efficiency in all cases, but tests on typical image compression tasks have shown that this method offers on average an 81.16 percent reduction in the total number of multiplies, additions and subtractions required as compared to full search.