This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the mult...

A swarm is a ‘‘complex adaptive system’’, which is decentralized and self-organized and whose individuals are simple, homogeneous and autonomous. Swarm intelligence is defined to describe its emergent behaviors. Both wireless sensor networks and mobile multi-robots demonstrate swarm features. This paper first discusses the challenges of combining wireless sensor networks and mobile multi-robots...

Midges (Anarete pritchardi) coordinate their flight motions to form a cohesive group during swarming. In this paper, individual midge motion dynamics, sensing abilities, and flight rules are represented with a midge swarm model. The sensing accuracy and flight rule are adjusted so that the model produces trajectory behavior, and velocity, speed, and acceleration distributions, that are remarkab...

by MICHAEL ANTHONY KOVACINA This work implements and demonstrates a set of tools to automatically generate swarm algorithms. SWEEP, SWarm Experimentation and Evaluation Platform, a swarm algorithm development and simulation platform is implemented. To demonstrate the utility of SWEEP, several swarm-based algorithms are constructed for free-moving and dynamics-constrained agents, culminating in ...

In this paper, we propose three specific scenarios in order to allow one to analyze the performance of swarmintelligence based coordination models for swarm of robots. The specific scenarios aim to assess some features presented on swarm robots: (i) contraction and expansion; (ii) selfsegregation and self-aggregation; and (iii) the capacity to change abruptly the fly direction whenever it is ne...

Standard particle swarm optimization is easy to fall into local optimum and has the problem of low precision. To solve these problems, the paper proposes an effective approach, called particle swarm optimization based on multiple swarms and opposition-based learning, which divides swarm into two subswarms. The 1st sub-swarm employs PSO evolution model in order to hold the self-learning ability;...

Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. The discipline focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. Examples of systems studied by swarm intelligence are colonies ...

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized of reinforcement learning with hybrid evolutionary algorithms. hybridizes different swarm algorithms such as particle optimization, evolution strategies, simulated annealing, differential evolution, modular to account for other three by storing their solutions in shared memory, the...

We present a new robotic concept, called SWARM-BOT, based on a swarm of small and simple autonomous mobile robots called S-BOTs. S-BOTs have a particular assembling capability that allows them to connect physically to other S-BOTs and form a bigger robot entity, the SWARM-BOT. A SWARM-BOT is typically composed by 10 to 30 S-BOTs physically interconnected. S-BOTs can autonomously assemble into a...