Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

Proton Exchange Membrane Fuel Cell Modeling Based on Seeker Optimization Algorithm

Seeker optimization algorithm （SOA） has applications in continuous space of swarm intelligence. In the fields of proton exchange membrane fuel cell （PEMFC） modeling, SOA was proposed to research a set of optimized parameters in PEMFC polarization curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10^-23. Hence, the optimization model could fit the experiment data with high precision.

Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media;

World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,etc.Sentiment Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and neutral.However,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications.So,there is a need exists to develop a proper technique for both identification and classification of sentiments in social media.To get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability.The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social media.The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media.In order to attain this,SOADL-SAC model carries out data preprocessing to clean the input data.In addition,Glove technique is applied to generate the feature vectors.Moreover,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the sentiments.Finally,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results.In order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark datasets.The experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.