his paper presents the review on image edge detection by using particle swarm optimization and gravitational search algorithm. The paper present the various works done by particle swarm optimization and gravitational search algorithm in the field of digital image processing. By observing the previous works the two optimization algorithms particle swarm optimization algorithm and gravitational s...

A total of 1099 data points consisting of alcohol-alcohol, alcohol-alkane, alkane-alkane, alcohol-amine and acid-acid binary solutions were collected from scientific literature to develop an appropriate artificial neural network (ANN) model. Temperature, molecular weight of the pure components, mole fraction of one component and the structural groups of the components were used as input paramet...

Feature selection is a preprocessing technique with great importance in the fields of data analysis, information retrieval processing, pattern classification, and data mining applications. It process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acc...

The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing,...

In this chapter, Deep Memory with Particle Swarm Optimization (DMPSO) algorithm is presented, which is based on Particle Swarm Optimization initialized by the particles of Deep Memory Greedy Search (DMGS). The Particle Swarm Optimization (PSO) is a population based optimization technique, where the population is called a swarm. In PSO, each particle represents a possible solution to the optimiz...

The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing,...

The aim of this paper is to explore the binary sequences with high discrimination using Particle Swarm Optimization with Cauchy Mutation (PSOCM) technique. The cross-ambiguity function method is used to detect the stationary and moving targets in various target detection scenarios. The contour plots of the cross-ambiguity function are computed as a function of delay and Doppler frequency shift....

for a uniform distribution of water, decrease in water waste and decrease in erosion of soil, it is important that a land be prepared with proper slopes along its length as well as width. the aim of leveling is to create appropriate slopes for irrigation and drainage on the lands that were not already properly levelled and of the same time creating the level surface with a minimum transport of ...

The stochastic and intermittent characteristic of wind power present great challenge to the operation of power system. A novel unit commitment problem (UCP) model is proposed in this paper. As the status of thermal units need to be described by binary number, Binary Particle Swarm Optimization (BPSO) algorithm is proposed to find the optimum schedule scheme. Case studies on the 10 units system ...

A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show th...