The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a hybrid algorithm comb...

Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. In this case, global optimization techniques are required in order to avoid the local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently ...

Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm Optimization (APSO) is proposed to inc...

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...

Massive multiple input multiple output antenna array is crucial for the fifth generation wireless communication. Proper antenna array design can reduce interference among different signals and generate desirable beamforming. Sparse antenna array is able to form narrower beam with lower sidelobe than equally spaced antenna array given the same number of array elements. However, determining the p...

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is ...

This research focuses on continuous dimensional affect recognition from bodily expressions using feature optimization and adaptive regression. Both static posture and dynamic motion bodily features are extracted in this research. A hybrid particle swarm optimization (PSO) algorithm is proposed for feature selection, which overcomes premature convergence and local optimum trap encountered by con...

In order to overcome the drawbacks of falling into local extremum and lower optimization precision of standard particle swarm optimization (PSO) algorithm, multipopulation strategy, adaptive dynamic adjustment strategy and co-evolution mode are introduced into the standard PSO algorithm in order to propose an improved coevolutionary PSO(MPACEPSO) algorithm based on multi-strategy evolution mode...

Particle swarm optimization(PSO) algorithm has the advantages of simplicity and easy implementation, but it exits the weaknesses of the being easy to fall into local minimum and premature convergence. In order to overcome these weaknesses of PSO algorithm, the inertia weight and learning factor are improved and the PSO algorithm is initialized by using chaotic optimization in order to propose a...