Mounir Ben Ghalia

Regrouping particle swarm optimization: A new global optimization algorithm with improved performance consistency across benchmarks

Particle swarm optimization (PSO) is known to suffer from stagnation once particles have prematurely converged to any particular region of the search space. The proposed regrouping PSO (RegPSO) avoids the stagnation problem by automatically triggering swarm regrouping when premature convergence is detected. This mechanism liberates particles from sub-optimal solutions and enables continued progress toward the true global minimum. Particles are regrouped within a range on each dimension proportional to the degree of uncertainty implied by the maximum deviation of any particle from the globally best position. This is a computationally simple yet effective addition to the computationally simple PSO algorithm. Experimental results show that the proposed RegPSO successfully reduces each popular benchmark tested to its approximate global minimum.

Tracking control of multiple nonlinear systems via information interchange

This paper considers tracking control of multiple nonlinear systems with a desired trajectory which is not available to each system. With the aid of information interchange between systems, distributed robust/adaptive control laws are proposed such that the state of each system asymptotically converges to the desired trajectory. Simulation results show the effectiveness of the proposed control laws.

An integrated neural network-based vision system for automated separation of clods from agricultural produce

Abstract Mechanical harvesting of onion and other agricultural produce from below ground has not been quite successful due to a large number of factors affecting the performance of harvesters. This paper discusses the integration of a neural network-based vision system with mechanical harvesters for separation of onion from soil clod to improve the efficiency of mechanical separator system. The vision system consists of a multi-layer neural network classifier that maps textural features computed from gray-scale images of onions and clods into the right object. Texture features were computed from co-occurrence matrices that specify the spatial relationship between gray-levels in the image. The textural features selected for this application consist of homogeneity, energy, contrast, and variance. The network was trained using the back-propagation algorithm. Based on this textural feature classification, the effect of changing the network configuration on separation effectiveness (SE) was also characterized. Factors including network topology and combination of textural feature measures forming the inputs of the network were systematically analyzed. It has been demonstrated that integration of the neural network vision system with mechanical harvester significantly improves the SE.

Distributed control of multiple wheeled mobile robots

This paper considers the cooperative control problem of multiple wheeled mobile robots. Cooperative control laws are proposed such that the state of each mobile robot asymptotically tracks a desired trajectory under the condition that the desired trajectory is only available to a portion of a group of mobile robots. Simulation results show the effectiveness of the proposed control laws.