Image segmentation is one of the most critical tasks in Magnetic Resonance (MR) images analysis. Since the performance of most current image segmentation methods is suffered by noise and intensity non-uniformity artifact (INU), a precise and artifact resistant method is desired. In this work, we propose a new segmentation method combining a new Hidden Markov Random Field (HMRF) model and a novel hybrid metaheuristic method based on Cuckoo search (CS) and Particle swarm optimization algorithms (PSO). The new model uses adaptive parameters to allow balancing between the segmented components of the model. In addition, to improve the quality of searching solutions in the Maximum a posteriori (MAP) estimation of the HMRF model, the hybrid metaheuristic algorithm is introduced. This algorithm takes into account both the advantages of CS and PSO algorithms in searching ability by cooperating them with the same population in a parallel way and with a solution selection mechanism. Since CS and PSO are performing exploration and exploitation in the search space, respectively, hybridizing them in an intelligent way can provide better solutions in terms of quality. Furthermore, initialization of the population is carefully taken into account to improve the performance of the proposed method. The whole algorithm is evaluated on benchmark images including both the simulated and real MR brain images. Experimental results show that the proposed method can achieve satisfactory performance for images with noise and intensity inhomogeneity, and provides better results than its considered competitors.

The authors have proposed a new automatic classification scheme based on the conjoint use of the multi‐layer perceptron (MLP) neural network and an enhanced particle swarm optimisation (EPSO) algorithm for its training. In this work, six predominant categories of heartbeats from MIT‐BIH database are considered, which are: normal, premature ventricular contraction, atrial premature contraction, right bundle branch block, left bundle branch block and paced beats. First, the authors have applied the standard particle swarm optimisation (PSO) algorithm to select the network structure for each features vector. Then, the relevant electrocardiogram (ECG) features to the studied arrhythmias were chosen, which suited to the optimised training performance of the classifier. The recognition performance of the proposed EPSO‐MLP classification system is evaluated considering two different versions of the EPSO algorithm. In the first version (EPSOw), the inertia weight factor of the PSO algorithm is proposed to be a variable with iterations. However, two PSO parameters are taken to be variables in the second version of the improved learning algorithm (EPSOwc). The obtained experimental results prove the enhancement of the convergence ability of the MLP neural network and confirm the superiority of the proposed EPSO‐MLP classification scheme on comparison with the other last published classification systems.

In this work, we deal with a class of problem of trajectory planning taking into account the smoothness of the trajectory. We assume that we have a set of positions in which the robot must pass. These positions are not assigned in the time axis. This kind of result can be found in many works of trajectory planning. Thus, this result is not complete in the sense that we do not have the complete trajectory and, we do not have any idea about the whole time to move. In this work, we propose a formulation of this problem, where the total length of the trajectory and the total time to move from the initial to the final position are minimized simultaneously. In order to avoid abrupt movement, we should ensure the smoothness of the trajectory at the position, the velocity and the acceleration levels. Thus, the position function must be at least two times differentiable. We use a genetic algorithm to resolve this problem and we show the efficiency of the proposed technique by simulation.

Many real-world optimization problems are dynamic (time dependent) and require an algorithm that is able to continuously track a changing optimum over time. In this paper, we investigate a recently proposed algorithm for dynamic continuous optimization, called MLSDO (Multiple Local Search algorithm for Dynamic Optimization). MLSDO is based on several coordinated local search agents and on the archiving of the optima found over time. This archive is used when a change occurs in the objective function. The performance of the algorithm is evaluated on the set of benchmark functions provided for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems.

In this paper, we propose a multiagent based evolution strategy algorithm, called CMADO, to evaluate the amplitudes of the deformations of the walls of the third cerebral ventricle on a brain cine-MR imaging. CMADO based segmentation technique is applied on a 2D+t dataset to detect the contours of the region of interest (i.e. lamina terminalis). Then, the successive segmented contours are matched using a procedure of global alignment. Finally, local measurements of deformations are derived from the previously determined matched contours. The validation step is realized by comparing our results to the measurements achieved on the same patients through a manual segmentation provided by an expert using Ethovision ®software.

Many real-world problems are dynamic and require an optimization algorithm that is able to continuously track a changing optimum over time. In this paper, a new multiagent algorithm for solving dynamic problems is studied. This algorithm, called MADO, is analyzed using the Moving Peaks Benchmark, and its performances are compared to those of competing dynamic optimization algorithms on several instances of this benchmark. The obtained results show the efficiency of MADO, even in multimodal environments.

For the analytic on-line recognition of handwriting, the range of pattern recognition problems can be described by the severity of letter segmentation required. More difficult problems require an interaction of letter segmentation and recognition. These problems include overlapping discretely written characters, pure cursive writing, and mixed cursive and discrete writing. To these problems concerning the letter segmentation, the word segmentation problems is added. Since a script can contain numbers, capital letters as well as lowercase letters, it is necessary to have a system able to recognize them. This paper describes an on-line system for identifying and recognizing numeral characters and capital letters in handwriting sentences. This system provides two segmentation modules: the first one is to isolate the word drawings within a sentence, and the other one is to separate numeral characters and capital letters from a mixed writing prior to their recognition.