Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA a population-based metaheuristic algorithm that combines benefits memetics with particle swarm optimization. has been used various areas, especially engineering problems due to its implementation easiness limited variables. Many improvements have made alleviate dr...

Abstract We present an analysis of survey observations the trailing L 5 Jupiter Trojan swarm using wide-field Hyper Suprime-Cam CCD camera on 8.2 m Subaru Telescope. detected 189 Trojans from our that covered about 15 deg 2 sky with a detection limit r = 24.1 mag, and selected unbiased sample consisting 87 objects absolute magnitude 14 ? H ? 17 corresponding to diameter km D 10 for size distrib...

In this paper, we propose a high capacity data hiding method applying in binary images. Since image has only two colors, black or white, it is hard to hide imperceptible. The capacities and imperception are always trade-off problem. Before embedding shuffle the secret by pseudo-random number generator keep more secure. We divide host into several non-overlapping (2n+1) sub-blocks an M N as many...

This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and optimization. Whereas optimization has been popular academic topic for decades, swarm intelligence is relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed i...

In research on eye-movement control during reading, the importance of cognitive processes related to language comprehension relative to visuomotor aspects of saccade generation is the topic of an ongoing debate. Here we investigate various eye-movement measures during reading of randomly shuffled meaningless text as compared to normal meaningful text. To ensure processing of the material, reade...

A Fractal Evolutionary Particle Swarm Optimization (FEPSO) is proposed based on the classical particle swarm optimization (PSO) algorithm. FEPSO applies the fractal Brownian motion model used to describe the irregular movement characteristics to simulate the optimization process varying in unknown mode, and include the implied trends to go to the global optimum. This will help the individual to...

In this paper, we present an extension of the heuristic called “particle swarm optimization” (PSO) that is able to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle and is based on the idea of having a set of subswarms instead of single particles. In each sub-swarm, a PSO algorithm is executed and, a...

As an emergent research area by which swarm intelligence is applied to multi-robot systems; swarm robotics (a very particular and peculiar sub-area of collective robotics) studies how to coordinate large groups of relatively simple robots through the use of local rules. It focuses on studying the design of large amount of relatively simple robots, their physical bodies and their controlling beh...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...