A hybrid OBL-based firefly algorithm with symbiotic organisms search algorithm for solving continuous optimization problems

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions.

     

A wrapper‐based feature selection for improving performance of intrusion detection systems

Along with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches.     

Design of a reversible structure for memory in quantum-dot cellular automata

Complementary metal oxide semiconductor (CMOS) technology has limitations in reducing the area and size of circuits. The disadvantages of this technology include high power consumption and temperature problems. Quantum-dot cellular automata (QCA) is a new technology that can overcome these shortcomings. Reversible logic is technology used to reduce the power loss in QCA. QCA can be used to design memories that require high operating speed. In this paper, we propose a structure for the reversible memory in QCA. The proposed structure utilizes three-layer technology, which has a significant impact on circuit size reduction. The proposed structure for the reversible memory has 63% improvement in cell number, a 75% improvement in area occupancy, and a 60% reduction in delay compared to the previous best structure.     

A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology

Applied Intelligence - Feature selection plays a key role in data mining and machine learning algorithms to reduce the processing time and increase the accuracy of classification of high...     

An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering

Multimedia Tools and Applications - Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to...     

A novel hybrid whale optimization algorithm with flower pollination algorithm for feature selection: Case study Email spam detection

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection.     

An improved cuckoo search optimization algorithm with genetic algorithm for community detection in complex networks

This paper improved Cuckoo Search Optimization (CSO) algorithm with a Genetic Algorithm (GA) for community detection in complex networks. CSO algorithm has problems such as premature convergence, delayed convergence, and getting trapped in the local trap. GA has been quite successful in terms of community detection in complex networks to increase exploration and exploitation. GA operators have been used dynamically in order to increase the speed and accuracy of the CSO. The number of populations is dynamically adjusted based on the amount of exploration and exploitation. Modularity objective function (Q) and Normalized Mutual Information (NMI) is used as an optimization function. It was carried out on six types of real complex networks. The proposed algorithm was tested with GA, Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), and CSO, with different iterations in modularity and NMI criteria. The results show that in most comparisons, the proposed algorithm has been more successful than the basic comparative algorithms, and it has proven its superiority in terms of modularity and NMI. The proposed algorithm performed an average of 54% better in modularity and 88% in NMI than other algorithms. It performed on average in modularity criteria 84.3%, 58.8%, 33.7% and 38.8%, respectively, compared to CSO, ABS, GWO and GA algorithms, and in terms of NMI index, 188.7%, 39.1%, 52.3% and 73.8%, respectively in CSO, ABS, GWO and GA algorithms performed better.

     

An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems

The Journal of Supercomputing - Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features....     

Enriched Latent Dirichlet Allocation for Sentiment Analysis

One of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other parameters. In this study, three novel topic models called date sentiment LDA (DSLDA), author–date sentiment LDA (ADSLDA), and pack–author–date sentiment LDA (PADSLDA) are proposed. The proposed models extend LDA through some extra parameters such as date, author, helpfulness, sentiment, and subtopic. The proposed models use helpfulness in the Gibbs sampling algorithm. Helpfulness is a part of readers who found the review helpful. The proposed models divide the words into two categories: the words more affected by the distribution of subtopic and the words more affected by the main topic. In this study, a new concept called pack is introduced, and a new model called PADSLDA is proposed for sentiment analysis at pack level. The proposed models outperformed the baseline models because according to evaluations results, the extra parameters can appropriately affect the generating process of words in a review. Sentiment analysis at the document level, perplexity, and topic coherence are the main parameters used in the evaluations.     

A multi-objective optimization algorithm for feature selection problems

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.