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Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D2PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LODpBest and LODgBest. Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.

D 3 G 2 A is a new multi-agent approach which addresses additive Constraint Satisfaction Problems (∑CSPs).This approach is inspired by the guided genetic algorithm (GGA) and by the Dynamic distributed double guided genetic algorithm for Max_CSPs. It consists of agents dynamically created and cooperating in order to solve problems with each agent performs its own GA. First, our approach is enhanced by many new parameters. These latter allow not only diversification but also escaping from local optima. Second, the GGAs performed agents will no longer be the same. This is stirred by NEO-DARWINISM theory and the nature laws. In fact our approach will let the agents able to count their own GA parameters. In order to show D 3 G 2 A advantages, the approach and the GGA are applied on the radio link frequency allocation problem (RLFAP) and on the randomly generated binary CSPs.

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