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Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuáis to get optimal solutions. Such co-operative model differs from competitive models in the way that individuáis die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

Ant colony systems have been widely employed in optimization issues primarily focused on path flnding optimization, such as travelling salesman problem. The main advantage lies in the choice of the edge to be explored, deflned using pheromone trails. This paper proposes the use of ant colony systems to explore a Backus-Naur form grammar whose elements are solutions to a given problem. Similar models, without using ant colonies, have been used to solve optimization problems or to automatically genérate programs such as grammatical swarm (based on particle swarm optimization) and grammatical evolution (based on genetic algorithms). This workpresents the application of proposed ant colony rule derivation algorithm and benchmarks this novel approach in a well-known deceptive problem, the Santa Fe Trail. Proposed algorithm opens the way to a new branch of research in swarm intelligence, which until now has been almost nonexistent, using ant colony algorithms to genérate solutions of a given problem described by a BNF grammar with the advantage of genotype/phenotype mapping, described in grammatical evolution. In this case, such mapping is performed based on the pheromone concentration for each production rule. The experimental results demónstrate proposed algorithm outperforms grammatical evolution algorithm in the Santa Fe Trail problem with higher success rates and better solutions in terms of the required steps.

Particle swarm optimization is a heuristic and stochastic technique inspiredby the flock of birds when looking for food. It is currently being used to solve continuous and discrete optimization problems. This paper proposes a hybrid, genetic inspired algorithm that uses random mutation/crossover operations and adds penalty functions to solve a particular case: the multidimensional knapsack problem. The algorithm implementation uses particle swarm for binary variables with a genetic operator. The particles update is performed in the following way: flrst using the iterative process (standard algorithm) described in the PSO algorithm and then using the best particle position (local) and the best global position to perform a random crossover/mutation with the original particle. The mutation and crossover operations speciflcally apply to personal and global best individuáis. The obtained results are promising compared to those obtained by using the probability binary particle swarm optimization algorithm.

The management and proper use of the Urban Public Transport Systems (UPTS) constitutes a critical field that has not been investigated in accordance to its relevance and urgent idiosyncrasy within the Smart Cities realm. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable, and self-organized behavior to deal with dynamic and fast changing systems. The intelligence of cities can be modelled as a swarm of digital telecommunication networks (the nerves), ubiquitously embedded intelligence, sensors and tags, and software. In this paper, a new approach based on the use of the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation and Fireworks algorithms to build a system that makes the complete control of the UPTS a tangible reality.