Bat algorithm (BA) is an eminent meta-heuristic that has been widely used to solve diverse kinds of optimization problems. BA leverages the echolocation feature bats produced by imitating bats’ searching behavior. faces premature convergence due its local search capability. Instead using standard uniform walk, Torus walk viewed as a promising alternative improve In this work, we proposed improved variation applying torus diversity and convergence. The proposed. Modern Computerized Algorithm...

Artificial neural networks (ANN) have been widely used in the field of data classification. Normally, training network is applied with traditional back propagation technique. As, this approach has various drawbacks, done Particle Swarm Optimization (PSO). PSO to solve diverse kind optimization problems. Population initialization performs a significant role meta-heuristic algorithms. This paper describes new population Log Logistic termed as PSOLL-NN create swarm. The proposed algorithm...

Bat algorithm (BA) is a nature-inspired metaheuristic which widely used to solve the real world global optimization problem. BA population-based intelligent stochastic search technique that emerged from echolocation features of bats and created mimics foraging behavior. One major issue faced by frequently captured in local optima while handling complex real-world problems. In this study, new variant named as improved bat (I-BAT) proposed. Improved modifies standard enhancing its exploitation...

Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in the recent years. A number of variants have established for that makes DE more applicable. However, most are suffering from problems convergence speed and local optima. novel tournament based parent selection variant proposed this research. The enhances searching capability improves algorithm. This paper also presents statistical comparison existing mutation...

Evolutionary and Swarm intelligence algorithms are widely used for global optimization problems. Bat Algorithm is one such algorithm based on a population-based metaheuristic algorithm. faces the premature convergence problem in which bats get stuck local optimum cannot reach safely. The initial population generation of plays an important role to move efficiently d-dimensional search space. Standard (StdBA) uses random distribution initialization bats. In this paper, we propose novel variant...

In the dental domain, periapical x-rays play a key role in finding tooth lesions. However, it's challenging for dentist to correctly predict existence of lesion as well its breed using radiographs due opaque disposition x-rays. This work aims at developing an application which can automatically detect type Two different experiments were performed this work. first experiment, features extracted pretrained network (Alexnet), and then on these conventional classifiers Support Vector Machine...

Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. BA has been mostly used to resolve diverse kind optimization problems and one major issue faced by frequently captured in local optima meanwhile handling complex real world problems. Many authors improved standard with different mutation strategies but an exhausted comprehensive overview about still lacking. This paper aims furnish concise...

Safety critical spare parts hold special importance for aviation organizations. However, accurate forecasting of such becomes challenging when the data are lumpy or intermittent. This research paper proposes an artificial neural network (ANN) model that is able to observe recent trends error surface and responds efficiently local gradient precise prediction results marked by lumpiness. Introduction momentum term allows proposed ANN ignore small variations in behave like a low-pass filter...

Artificial neural network (ANN) has a wide variety of practice for the solution problems in area data classification. Back propagation algorithm is famous (NN) traditional training approach. Hence, this classical technique many drawbacks like stuck local minima and maximum number iterations required. Particle Swam Optimization (PSO) been widely applied solutions classification problems. Population initialization vital factor PSO algorithm, which considerably influences diversity convergence...

Big data have emerged as one of the fascinating areas research in last few years. The demand for shows that it will further grow next years to come. mainly came into existence because rapid growth social media. Twitter has appeared be most popular media over Internet. receives tens millions tweets per day, creating huge unstructured form, a lot been carried out extract useful information from raw data. It also exhibits sentiment people on specific topics. However, this repository is and...

The main purpose of this paper is that how to make Artificial Neural Networks (ANN) dynamic in the sense it can decide which architecture from given set has optimal results.For we need use some optimization technique get optimum ANN.In Particle Swarm Optimization Technique used.Particle (PSO) applied variety problems and provides good results.In research different techniques for ANN applications are reviewed.Researchers have proposed optimizations but still there required efficient...

A neural network-based method is presented for the diagnosis and classification of patients infected with hepatotropic viral disease. The non-invasive approach makes use real-time pathological data in form liver function tests specific virological markers during training. unknown patients, not included training set, are then processed through trained network model. Experimental results demonstrate that proposed able to effectively diagnose disease classifies its stage be acute, chronic or...

Symmetry in a differential evolution (DE) transforms solution without impacting the family of solutions. For symmetrical problems equations, DE is strong evolutionary algorithm that provides powerful to resolve global optimization problems. DE/best/1 and DE/rand/1 are two most commonly used mutation strategies DE. The former better exploitation while latter ensures exploration. DE/Neighbor/1 an improved form maintain balance between exploration which was with random neighbor-based (RNDE)...

This paper presents a novel random controlled pool base differential evolution algorithm (RCPDE) where powerful mutation strategy and control parameter pools have been used. The p...

Differential Evolution (DE) is a simple, powerful and easy to use global optimization algorithm. DE has been studied in detail by many researchers the past years. In algorithm trial vector generation strategies have significant influence on its performance. This research studies that whether performance of can be improved incorporating selection advancement effective strategies. A novel proposed this speeds up convergence speed The fitness proportion based random (FPRVDE) individual...