Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm

Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) programming has been coined as a prominent logical rule that defines the identity of RBFNN. In this research, a swarm-based searching algorithm namely, the Artificial Bee Colony (ABC) will be introduced to facilitate the training of RBFNN. Worth mentioning that ABC is a new population-based metaheuristics algorithm inspired by the intelligent comportment of the honey bee hives. The optimization pattern in ABC was found fruitful in RBFNN since ABC reduces the complexity of the RBFNN in optimizing important parameters. The effectiveness of ABC in RBFNN has been examined in terms of various performance evaluations. Therefore, the simulation has proved that the ABC complied efficiently in tandem with the Radial Basis Neural Network with 2SAT according to various evaluations such as the Root Mean Square Error (RMSE), Sum of Squares Error (SSE), Mean Absolute Percentage Error (MAPE), and CPU Time. Overall, the experimental results have demonstrated the capability of ABC in enhancing the learning phase of RBFNN-2SAT as compared to the Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm

Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis

Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review

The challenge of finding a global optimum in a solution search space with limited resources and higher accuracy has given rise to several optimization algorithms. Generally, the gradient-based optimizers converge to the global solution very accurately, but they often require a large number of iterations to find the solution. Researchers took inspiration from different natural phenomena and behaviours of many living organisms to develop algorithms that can solve optimization problems much quicker with high accuracy. These algorithms are called nature-inspired meta-heuristic optimization algorithms. These can be used for denoising signals, updating weights in a deep neural network, and many other cases. In the state-of-the-art, there are no systematic reviews available that have discussed the applications of nature-inspired algorithms on biomedical signal processing. The paper solves that gap by discussing the applications of such algorithms in biomedical signal processing and also provides an updated survey of the application of these algorithms in biomedical image processing. The paper reviews 28 latest peer-reviewed relevant articles and 26 nature-inspired algorithms and segregates them into thoroughly explored, lesser explored and unexplored categories intending to help readers understand the reliability and exploration stage of each of these algorithms

Website Phishing Technique Classification Detection with HSSJAYA Based MLP Training

Website phishing technique is the process of stealing personal information (ID number, social media account information, credit card information etc.) of target users through fake websites that are similar to reality by users who do not have good intentions. There are multiple methods in detecting website phishing technique and one of them is multilayer perceptron (MLP), a type of artificial neural networks. The MLP occurs with at least three layers, the input, at least one hidden layer and the output. Data on the network must be trained by passing over neurons. There are multiple techniques in training the network, one of which is training with metaheuristic algorithms. Metaheuristic algorithms that aim to develop more effective hybrid algorithms by combining the good and successful aspects of more than one algorithm are algorithms inspired by nature. In this study, MLP was trained with Hybrid Salp Swarm Jaya (HSSJAYA) and used to determine whether websites are suspicious, phishing or legal. In order to compare the success of MLP trained with hybrid algorithm, Salp Swarm Algorithm (SSA) and Jaya (JAYA) were compared with MLPs trained with Cuckoo Algorithm (CS), Genetic Algorithm (GA) and Firefly Algorithm (FFA). As a result of the experimental and statistical analysis, it was determined that the MLP trained with HSSJAYA was successful in detecting the website phishing technique according to the results of other algorithms

An advanced short-term wind power forecasting framework based on the optimized deep neural network models

With the continued growth of wind power penetration into conventional power grid systems, wind power forecasting plays an increasingly competitive role in organizing and deploying electrical and energy systems. The wind power time series, though, often present non-linear and non-stationary characteristics, allowing them quite challenging to estimate precisely. The aim of this paper is in proposing a novel hybrid model named Evol-CNN in order to predict the short-term wind power at 10-min interval up to 3-hr based on deep convolutional neural network (CNN) and evolutionary search optimizer. Specifically, we develop an improved version of Grey Wolf Optimization (GWO) algorithm by incorporating two effective modifications in its original structure. The proposed GWO algorithm is more effective than the original version due to performing in a faster way and the ability to escape from local optima. The proposed GWO algorithm is utilized to find the optimal values of hyperparameters for deep CNN model. Moreover, the optimal CNN model is employed to predict wind power time series. The main advantage of the proposed Evol-CNN model is to enhance the capability of time series forecasting models in obtaining more accurate predictions. Several forecasting benchmarks are compared with the Evol-CNN model to address its effectiveness. The simulation results indicate that the Evol-CNN has a significant advantage over the competitive benchmarks and also, has the minimum error regarding of 10-min, 1-hr and 3-hr ahead forecasting.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Review and Classification of Bio-inspired Algorithms and Their Applications

Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms

Optimal Pattern Synthesis of Linear Antenna Array Using Grey Wolf Optimization Algorithm

The aim of this paper is to introduce the grey wolf optimization (GWO) algorithm to the electromagnetics and antenna community. GWO is a new nature-inspired metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It has potential to exhibit high performance in solving not only unconstrained but also constrained optimization problems. In this work, GWO has been applied to linear antenna arrays for optimal pattern synthesis in the following ways: by optimizing the antenna positions while assuming uniform excitation and by optimizing the antenna current amplitudes while assuming spacing and phase as that of uniform array. GWO is used to achieve an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. GWO is also applied for the minimization of the first side lobe nearest to the main beam (near side lobe). Various examples are presented that illustrate the application of GWO for linear array optimization and, subsequently, the results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired evolutionary algorithms. The results suggest that optimization of linear antenna arrays using GWO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques