International Journal of Computational Intelligence and Applications

In order to improve shipboard power system dynamic stability, two bio-inspired algorithms, the genetic algorithm (GA) and quantum-behaved particle swarm optimization (QPSO), method are proposed for the shipboard power system stabilizer (PSS) optimization. The proposed PSS optimization method is inspired by a hybrid-coordinated stabilizer for diesel engine generator and the bio-inspired algorithm. The simulations are conducted under load change disturbance and short-circuit fault case for the marine generator with/without the diesel engine speed governor. Simulation results show that the quantum particle swarm optimize strategy could improve the dynamic performance of the marine generator better than the GA method. The dynamic performance for shipboard power system always indicates the effectiveness, feasibility and robustness of the proposed approach.

As a core subtask in anaphora resolution, anaphoricity determination has aroused the interest of researchers. However, in recent work, the influence caused by the deep semantic information and the context of the coreference elements have not been taken into account. In this paper, by combining the semantic feature of Uygur, we established a Convolutional Neural Network & Long Short-Term Memory (CNN_LSTM) model in determining the anaphoricity of Uygur pronoun. Firstly, the deep negative semantic feature representation is extracted via word2vec. Secondly, the shallow explicit feature representation of coreference elements is extracted by our system. Afterwards, two kinds of features are combined to recognize whether coreference element is referential or not. The results showed that the method we used can distinguish coreference element accurately, the ACC${}^{+}$ score is 90.18% and the ACC${}^{-}$ score is 89.93%, which are higher than ANN (Artificial Neural Network) and SVM (Support Vector Machine) respectively.

Firefly algorithm is a swarm based algorithm that can be used for solving optimization problems. This paper proposed an improved fuzzy adaptive firefly algorithm (FAFA). In the proposed FAFA, a fuzzy system is used to adapt Firefly Algorithm’s parameters in order to improve its ability in global and local searches. Also, we used different fireflies initializing intervals and different iteration numbers to show the algorithm capability to find global optima. Results focus on the two case study categories of function optimization (seven benchmark functions) and presented a novel optimal multilevel thresholding approach for histogram-based image segmentation by using proposed FAFA and Otsu method. Evidence indicates that the optimization results of proposed FAFA approach are so better than the standard FA.

This paper introduces a novel algorithm called variable population size artificial bee colony (VPS-ABC) optimization algorithm. VPS-ABC is proposed to overcome the impact of the effect of initial population and improve the convergence rate of classical ABC. The main idea is based on reducing the number of food sources gradually and moving the bees towards the global best food source in each re-initialization process. Moreover, an analysis for convergence of the ABC algorithm is proofed in details. The convergence analysis is based on the relation between ABC variants and the general solution of the food source regeneration equation. To show the fitness of the proposed algorithm, a comparison is made between VPS-ABC versus classical ABC, PSO, and GA algorithms in tuning the proportional-integral-derivative (PID) controllers. Simulation results show that VPS-ABC algorithm is highly competitive, often outperforming PSO and GA algorithms.

In the current digitalized era, large datasets play a vital role in features extractions, information processing, knowledge mining and management. Sometimes, existing mining approaches are not sufficient to handle large volume of datasets. Biological data processing also suffers for the same issue. In the present work, a classification process is carried out on large volume of exons and introns from a set of raw data. The proposed work is designed into two parts as pre-processing and mapping-based classification. For pre-processing, three filtering techniques have been used. However, these traditional filtering techniques face difficulties for large datasets due to the long required time during large data processing as well as the large required memory size. In this regard, a mapping-based neural skyline filtering approach is designed. Randomized algorithm performed the mapping for large volume of datasets based on objective function. The objective function determines the randomized size of the datasets according to the homogeneity. Around 200 million DNA base pairs have been used for experimental analysis. Experimental result shows that mapping centric filtering outperforms other filtering techniques during large data processing.

Wireless sensor networking is a promising technology that can lead to automatic, intelligent, easier and more secure systems. A wireless sensor network (WSN) consists of small battery powered devices with limited energy resources. One of the major challenges in WSN lies in the energy constraint and computation resources available at the sensor nodes. One way to achieve energy efficiency would be through the use of a clustering technique. In this paper, we propose computational intelligence (CI) approaches to deal with the problem of sensor nodes clustering in a WSN with the ultimate goal to reduce energy expenditures and thus to extend the lifetime of the network. The main motivation is that CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures, and scenario changes. The main features of the proposed work span over two aspects. First, four metaheuristics have been adapted to deal with the tackled problem namely genetic algorithms, evolution strategies, particle swarm optimization and artificial bees colony. In this context, a suitable solution representation scheme has been developed and accordingly the algorithms operators, and overall dynamics have been defined. Second, the four developed algorithms are combined with an exhaustive search that is triggered whenever the number of alive sensor nodes drops below a given threshold. The performance of the proposed algorithms has been assessed and compared to the most known state of the art clustering based method namely low energy adaptive clustering hierarchy algorithm (LEACH). The obtained results show that the proposed approaches optimize in an efficient manner the lifetime of WSNs. They also show that the proposed algorithms compete and even outperform LEACH algorithm.

Restoration work of archaeological artifacts broken into pieces is similar to putting together a jigsaw puzzle. The purpose of this study is to construct an intelligent computer assistance system to conveniently restore archaeological discoveries from some fragments. AReal-Coded Genetic Algorithm (RCGA) was applicable for solving the positioning problem of a three-dimensional (3D) restoration. The fitness function value for RCGA was calculated from image similarity between the target and correct patterns in plane images at multiple camera angles. Image features of a 3D object were obtained by the ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and Accelerated KAZE (AKAZE) techniques; they were considered as a part of the fitness function value. Simulation study revealed that the RCGA approach was capable of automatically and efficiently adjusting the positions of 3D fragments, especially in the AKAZE technique. A user interface with the functions of design drawing was also created to assist in repair work. The interactive assistance interface for 3D restoration based on RCGA and followed by the hill-climbing algorithm would be applied to practical applications for digital archives of artifacts.