In this paper a novel invisible robust watermarking scheme for embedding and extracting a digital watermark in an image is presented. The novelty lies in determining perceptually important coefficients of transform in the host image using simple Haar Wavelet Transform (HWT) and Genetic Algorithm(GA)-Particle Swarm Optimization(PSO) based hybrid optimization. Invisible watermark is embedded such...

Particle Swarm Optimization (PSO), an evolutionary algorithm for optimization is extended to determine if natural selection, or survival-of-thefittest, can enhance the ability of the PSO algorithm to escape from local optima. To simulate selection, many simultaneous, parallel PSO algorithms, each one a swarm, operate on a test problem. Simple rules are developed to implement selection. The abil...

In the paper a modified particle swarm optimization (MPSO) is proposed where concepts from firefly algorithm (FA) are borrowed to enhance the performance of particle swarm optimization (PSO). The modifications focus on the velocity vectors of the PSO, which fully use beneficial information of the position of particles and increase randomization item in the PSO. Finally, the performance of the p...

Many scientific, engineering and economic problems involve optimization. In reaction to that, numerous optimization algorithms have been proposed. Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence which is inspired by concepts from ’Social Psychology’ and ’Artificial Life’. Essentially, PSO proposes that the co-operation of individuals promotes the evolution of the swarm...

Reader network planning (RNP) problem of radio frequency identification (RFID) system is a combinational optimization problem. In this study, we propose a genetic algorithm (GA) to solve this RNP problem. We have tested the proposed GA on several RNP problems and compare with a particle swarm optimization (PSO) method by solving the same RNPs. The comparison results demonstrate that the propose...

Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in...

Particle swarm optimization (PSO) is a novel swarm intelligent algorithm inspired by fish schooling and birds flocking. Due to the complex nature of engineering optimization tasks, the standard version can not always meet the optimization requirements. Therefore, in this paper, a new group decision mechanism is introduced to PSO to enhance the escaping capability from local optimum. Furthermore...

Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. In the present study an attempt is made to review the hybrid optimization techniques in which one main algorithm is a well known metaheuristic; particle swarm optimization or PSO. Hybridization is a method of combining two (or...

Particle swarm optimization (PSO) is an optimization approach from the field of artificial intelligence. A population of so-called particles moves through the parameter space defined by the optimization problem, searching for good solutions. Inspired by natural swarms, the movements of the swarm members depend on own experiences and on the experiences of adjacent particles. PSO algorithms are m...

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...