in this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. since cell formation problem is np-hard, two algorithms based on genetic and modified particle swarm optimization (mpso) algorithms are developed to solve the problem. for generating initial solutions in these algorithms, a new heuristic method is developed, which always cre...

-The social insect metaphor for solving diverse problems has become an emerging issue in the current years emphasizing on stochastic construction process building the solution probabilistically. Ant Colony Optimization (ACO) is an algorithm inspired by the foraging behavior of ants wherein ants deposits a volatile chemical call pheromone on the ground surface for the purpose of foraging and col...

We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation ...

We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation ...

− A swarm-intelligence solution to industrial problems of automatic multiple-faults diagnostics is proposed. In particular, drawbacks of swarm-based algorithms in heuristic search strategy related to the mutual dependence of solutions are overcome by a likelihood-based trail intensity modification of ant-colony optimization. Experimental results of comparison tests with an evolutionary state-of...

In this paper, a new model for the semi-obnoxious facility location problem is introduced. The new model is composed of a weighted minisum function to represent the transportation costs and a distance-based piecewise function to represent the obnoxious effects of the facility. A single-objective particle swarm optimizer (PSO) and a bi-objective PSO are devised to solve the problem. Results are ...

This article presents a hybrid evolutionary algorithm (PSABC) based on Artificial Bee Colony (ABC) and particle swarm optimization (PSO). Both of the algorithms are co-operative, population-based global search swarm intelligence met heuristics. The core of this algorithm is using PSO to optimize the fitness value of population in ABC. For Evaluation purpose, the proposed algorithm is tested on ...

Communication among agents in swarm intelligent systems and more generally in multiagent systems, is crucial in order to coordinate agents’ activities so that a particular goal at the collective level is achieved. From an agent’s perspective, the problem consists in establishing communication policies that determine what, when, and how to communicate with others. In general, communication polic...

This paper empirically evaluates four meta-heuristic search techniques namely particle swarm optimization, artificial bee colony algorithm, Genetic Algorithm and Big Bang Big Crunch Algorithm for automatic test data generation for procedure oriented programs using structural symbolic testing method. Test data is generated for each feasible path of the programs. Experiments on ten benchmark prog...

In this paper we investigate the potential for a swarm of winddriven Mars rovers (“Tumbleweeds”) to provide robust, autonomous exploration of rugged terrain. We propose new algorithms for collective intelligence, implemented via agent movement rules and mobile ad hoc network communication. We discuss the new algorithms and how they address the limitations of the current research, then present i...