the cuckoo search algorithm is a recently developedmeta-heuristic optimization algorithm, which is suitable forsolving optimization problems. to enhance the accuracy andconvergence rate of this algorithm, an improved cuckoo searchalgorithm is proposed in this paper. normally, the parametersof the cuckoo search are kept constant. this may lead todecreasing the efficiency of the algorithm. to cop...

Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective (MaOPs) are two classes of the optimization field that have potential applications in engineering. Modified Evolutionary Algorithms hybrid approaches seem to be suitable effectively deal with such problems. However, standard Crow Search Algorithm has not been considered for either DMOPs or MaOPs date. This paper proposes a...

cost optimization of the reinforced concrete cantilever soil retaining wall of a given height satisfying some structural and geotechnical design constraints is performed utilizing harmony search and improved harmony search algorithms. the objective function considered is the cost of the structure, and design is based on aci 318-05. this function is minimized subjected to design constraints. a n...

today, with rapid growth of the world wide web and creation of internet sites and online text resources, text summarization issue is highly attended by various researchers. extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. when, we are facing into large data volume documents, the extr...

Abstract The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For problems, selecting most relevant features of a dataset leads better accuracy with low training time. In this work, hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed FS on wrappe...

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

augmented downhill simplex method (adsm) is introduced here, that is a heuristic combination of downhill simplex method (dsm) with random search algorithm. in fact, dsm is an interpretable nonlinear local optimization method. however, it is a local exploitation algorithm; so, it can be trapped in a local minimum. in contrast, random search is a global exploration, but less efficient. here, rand...

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...