In this paper, we propose a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and on Simulated Annealing (SA) for reducing memory energy consumption in embedded systems. Our hybrid algorithm outperforms the Tabu Search (TS) approach. In fact, nearly from 76% up to 98% less energy consumption is recorded.

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...

Evolutionary computation (EC) techniques such as genetic algorithms (GAs), utilize multiple searching points in the solution space like PSO. Whereas GAs can treat combinatorial optimization problems, PSO was aimed to treat nonlinear optimization problems with continuous variables originally. Moreover, PSO has been expanded to handle combinatorial optimization problems and both discrete and cont...

In Evolutionary Algorithms (EA), elites are crucial to maintain good features in solutions. However, too many elites could make the evolutionary process stagnate and cannot enhance the performance. In this paper, we employ Particle Swarm Optimization (PSO) and Biogeography-Based Optimization (BBO) to propose a hybrid algorithm termed Biogeography-Based Particle Swarm Optimization (BPSO) which c...

This chapter discusses the workings of PSO in two research fields with special importance in real-world applications, namely noisy and dynamic environments. Noise simulation schemes are presented and experimental results on benchmark problems are reported. In addition, we present the application of PSO on a simulated real world problem, namely the particle identification by light scattering. Mo...

In this paper, we propose an automatic image annotation approach for region labeling that takes advantage of both context and semantics present in segmented images. The proposed approach is based on multi-class K-nearest neighbor, k-means and particle swarm optimization (PSO) algorithms for feature weighting, in conjunction with normalized cuts-based image segmentation technique. This hybrid ap...

Our main objective in this article is to achieve minimum side lobe levels for a specific first null beam-width and also a minimum size of the circumference by an optimization-based design method for non-uniform, planar, and circular antenna arrays. Our approach is based on a new variant of Particle swarm Optimization technique. This new technique is a hybrid of Local Neighborhood based PSO with...

This paper introduces hybridization of particle swarm optimization (PSO) with genetic algorithm (GA) denoted as PSO+GA provides an efficient approach which is used to solve non linear chaotic datasets. The proposed algorithm employed in probabilistic neural network(PNN) which is a variant of radial basic function artificial neural network (RBFANN) for finding precise value spread factor for acc...

In this paper, a two-phase hybrid particle swarm optimization algorithm (PRHPSO) is proposed for the permutation flow-shop scheduling problem (PFSP) with the minimizing makespan measure. The smallest position value (SPV) rule is used for encoding the particles that enable PSO for suitable PFSP, and the NEH and Tabu search algorithms are used for initializing the particles. In the first phase, t...

The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO-based algorithm is robust an...