In the recent past, several biological and natural phenomena have extensively attracted researchers towards the rapid development of science and engineering. Basically solving the optimization problems in various Engi...
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In the recent past, several biological and natural phenomena have extensively attracted researchers towards the rapid development of science and engineering. Basically solving the optimization problems in various Engineering discipline is a popular topic among the other problem solving strategies. Most of the biological processes include the swarm intelligence research areas where the activity and the behavior of real insects have been studied. One of the recently developed Swarm algorithms is the Honey Bee Mating optimization (HBMO) algorithm which is based on the mating behavior of bees. In this work, a hybrid metaheuristic honey bee mating based Pi-Sigma Neural Network (PSNN) have been proposed to successfully solve the classification problem of data mining. The proposed approach combines HBMO with the PSNN and is compared with other techniques like GA (Genetic algorithm), DE (Differential Evolution), and PSO (Particle Swarm optimization). Experimental results reveal that the proposed approach is steady as well as reliable and provides better classification accuracy than others.
Microgrids have been popularized in the past decade because of their ability to add distributed generation into the classic distribution systems. Protection problems are among several other problems that need solution...
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Microgrids have been popularized in the past decade because of their ability to add distributed generation into the classic distribution systems. Protection problems are among several other problems that need solutions in order to extract the full capability of these novel networks. This research follows the branches of two major solutions, namely adaptive protection and protection optimization. Adaptive protection implementation with a novel concept of clustering is considered, and protection setting optimization is done using a novel hybrid nature-inspiredalgorithm. Adaptive protection is utilized to cope with the topology variations, while optimization techniques are used to calculate the protection settings that provide faster fault clearances in coordination with backup protection. A modified IEEE 14 bus system is used as the test system. Validation was done for the fault clearing performance. The selected algorithm was effective than most other algorithms, and the clustering approach for adaptive overcurrent protection was able to reduce the number of relays' setting groups.
DNA microarray analysis plays a prominent role in classifying genes related to cancer. The dimension of the data is high and difficult to handle during classification. Hence, the dimension has to be reduced and highly...
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DNA microarray analysis plays a prominent role in classifying genes related to cancer. The dimension of the data is high and difficult to handle during classification. Hence, the dimension has to be reduced and highly predictive gene features must be obtained without affecting the accuracy. Previous studies concentrated either on improving the classification accuracy or reduction of gene features. Here, the multi-objective problem of obtaining reduced gene features with high classification accuracy is addressed using the proposed correlation feature selection filter and binary bat algorithm (BBA) with greedy crossover. The gene feature subsets are obtained using the correlation based feature selection filter and optimized using the BBA. Suboptimal solutions obtained due to pre-convergence of BBA are reset using the proposed greedy crossover. Highly predictive genes features are obtained and evaluated with support vector machine 10-fold cross-validation. An average classification accuracy of 95.85% with predictive gene features <1% of the total dataset was obtained when applied on cancer microarray datasets. The solution for the multi-objective problem of obtaining high classification accuracy with minimal number of genes is achieved with better performance over the existing algorithms. Also, the problem of pre-convergence with suboptimal solutions in optimizationalgorithms is overcome.
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