In this paper, a novel steganographic method, based on JPEG and Particle Swarm Optimization algorithm (PSO), is proposed. In order to improve the quality of stego-images, an optimal substitution matrix for transforming the secret messages is first derived by means of the PSO algorithm. The standard JPEG quantization table is also modified to contain more secret messages. The transformed message...

In this paper, we propose a particle swarm optimization (PSO) based hyper-heuristic algorithm for solving the resource constrained project scheduling problem (RCPSP). To the best of our knowledge, this is the first attempt to develop a PSO hyper-heuristic and apply to the classic RCPSP. The hyper-heuristic works as an upper-level algorithm that controls several low-level heuristics which operat...

The static task scheduling problem in distributed systems is important because of optimal usage of available machines and accepted computation time for scheduling algorithm. A PSO-based hybrid algorithm is presented to schedule the tasks represented by a Directed Acyclic Graph (DAG) to a bounded number of heterogeneous processors such that its schedule length is optimized. The algorithm first g...

─ Cuckoo Search (CS) is a new met heuristic algorithm. It is being used for solving optimization problem. It was developed in 2009 by XinShe Yang and Susah Deb. Uniqueness of this algorithm is the obligatory brood parasitism behavior of some cuckoo species along with the Levy Flight behavior of some birds and fruit flies. Cuckoo Hashing to Modified CS have also been discussed in this paper. CS ...

Particle swarm optimization (PSO) is a population based statistical optimization technique which is inspired by social behavior of bird flocking or fish schooling. The main weakness of PSO especially in multimodal problems is trapping in local minima. Recently a learning automata based PSO called PSO-LA to improve the performance of PSO has been reported. PSO-LA uses one learning automaton for ...

This paper presents a designing an optimal adaptive controller for tracking down the control of robot manipulators based on particle swarm optimization (PSO) algorithm. PSO algorithm has been used to optimize parameters of the controller and hence to minimize the integral square of errors (ISE) as a performance criteria. In this paper, an improved PSO using a logic is proposed to increase the c...

Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within multidimensional space. The explore space in an attempt to find minima or maxima problem. linked, and overall behavior particle swarm controlled by several parameters. PSO has been proposed as control strategy for physical swarms robots that are localizing source; analogous pa...

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The IPSO modified version of Particle (PSO) changing velocity position updates. five best solutions are employed to replace so-far each in update mechanism used previou...

The automatic upgradation of equalizer weights in channel equalization demands a low-complexity, highly accurate estimation recovery at the minimum possible time. low-complexity frequency domain improves mean square error (MMSE) process. Adding superiority particle swarm optimization (PSO) to coefficient selection process enhances MMSE. This work proposes frequency-domain along with modified PS...

Feature selection is an optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. Feature selection is of great importance in pattern classification, medical data processing, machine learning, and data mining applications. In this paper, continuous particle swarm optimization (PSO...