This paper proposes a refined version of particle swarm optimization technique for the optimum design of steel structures. Swarm is composed of a number of particles and each particle in the swarm represents a candidate solution of the optimum design problem. Design constraints in accordance with ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution) are imposed by t...

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at t...

Urban search and rescue is a difficult domain for autonomous mobile robots to operate in. The environment can be expected to be highly unstructured, with many obstacles and hazards for a robot to deal with. In addition, if human rescue teams are going to accept robotic assistance, they need to be assured that the robots are going to be helpful, not a hindrance. With these factors in mind, we ha...

Swarm robotics simply put is the study of robotic systems taking advantage of swarming or colony level behaviours similar to those found in social insects like Ants or Bees. The sum of the parts is greater than the whole. Heavy constraints are made on the mechanical and computational quality of an individual robot, to both reduce unit complexity and cost whilst maximising population size. Insec...

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which ...

A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework ...

a gyroscope is a device for measuring or maintaining orientation , based on the principles of conservation of angular momentum . the device is a spinning wheel or disk whose axle is free to take any orientation. this orientation changes much less in response to a given external torque than it would without the large angular momentum associated with the gyroscope's high rate of spin. since exter...

A framework for incorporating a swarm intelligent system with the Dynamic Data Driven Application System (DDDAS) is presented. Swarm intelligent systems, or artificial swarms, self-organize into useful emergent structures that are capable of solving complex problems, but are difficult to control and predict. The DDDAS concept utilizes repeated simulations of an executing application to improve ...

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavio...

This paper presents a method to employ particle swarm optimization in a split architecture injected with a plain ‘attractor’ configuration. This is achieved by splitting the input vector into two even sub-vectors, each of which is optimized in its own swarm. Then, a plain ‘attractor’ is injected into each swarm. The application of this technique to neural network training is investigated. Key-W...