The Swarm Application Framework (SAF) is a tool that makes the development of swarm applications more intuitive. Traditionally, swarm applications are created by programming several low-level rules. This approach leads to several problems in designing and testing swarms, which serve as inspiration for the features of SAF. SAF encourages a new paradigm for designing swarm applications: engineers...

It is a characteristic of swarm robotics that specifying overall emergent swarm behaviours in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation we need such specifications. This paper explores the use of temporal logic to formally specify, and possibly also prove, th...

Particle swarm optimization algorithm in solving complex functions, such as slow convergence, accuracy is not high, easily falling into local optimum problem. Based on the chaos optimization is introduced into particle swarm optimization algorithm, given the chaotic particle swarm optimization algorithm. In order to improve the image quality of CMOS image sensor, the image of the main noise sou...

This paper introduces Swarm AI, the first general framework for designing Swarm Intelligence approaches to problems. We outline principles of Swarm AI, discuss its connection to previous work, and analyze the advantages and disadvantages of this method. Finally, we describe a case study of applying Swarm AI to a new problem. Specifically, we discuss the design of a soccer team, using the princi...

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we pro...

This study examines a variable-length Particle Swarm Algorithm for Social Programming. The Grammatical Swarm algorithm is a form of Social Programming as it uses Particle Swarm Optimisation, a social swarm algorithm, for the automatic generation of programs. This study extends earlier work on a fixed-length incarnation of Grammatical Swarm, where each individual particle represents choices of p...

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...

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 ...