Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide varie...

Lyapunov methods have been successfully applied to compute the Region of Attraction (ROA) of a system, i.e. the set of states that remain invariant for all time. A popular method for constructing Lyapunov functions is the Sum of Squares (SOS) approach. Current methods for determining stability of hybrid systems using SOS require either stability over the entire hybrid guard or knowledge of the ...

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO...

Traditional numerical methods for time-harmonic acoustic scattering problems become prohibitively expensive in the high-frequency regime where the scatterer is large compared to the wavelength of the incident wave. In this paper we propose and analyse a hybrid boundary element method (BEM) for a class of non-convex polygonal scatterers. In this method the approximation space is enriched with os...

Particle Swarm Optimisation (PSO) is an intelligent search method based on swarm intelligence and has been widely used in many fields. However it is also easily trapped in local optima. In this paper, we propose two hybrid PSO algorithms: one uses a Differential Evolution (DE) operator to replace the standard PSO method for updating a particle’s position; and the other integrates both the DE op...

By integrating artificial bee colony and genetic algorithm, a novel hybrid swarm intelligent approach is proposed in this paper. The main idea of the approach is to obtain the parallel computation merit of GA and the speed and self-improvement merits of ABC by sharing information between GA population and bee colony. To exam the proposed method, it is applied to 4 benchmark functions for differ...

We propose a variant of the Simulated Annealing method for optimization in the multivariate analysis of diierentiable functions. The method uses the Hybrid Monte Carlo algorithm for the proposal of new conngurations. We show how this choice can improve the performance of simulated annealing methods by allowing much faster annealing schedules.

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using...

We present a class of methods which is a combination of the limited memory BFGS method and the truncated Newton method. Each member of the class is defined by the (possibly dynamic) number of vector pairs of the L-BFGS method and the forcing sequence of the truncated Newton method. We exemplify with a scheme which makes the hybrid method perform like the L-BFGS method far from the solution, and...