Asymptotic swarm stability problem of high-order dynamical multiagent systems is investigated in this paper. Consensus is a specific type of asymptotic swarm stability. Necessary and sufficient conditions are respectively presented for asymptotic swarm stability of two types of high-order dynamical multi-agent system models: general LTI systems and certain nonlinear dynamical multi-agent system...

Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rule...

A Multi-objective problems occurs wherever optimal solution necessary to be taken in the presence of tradeoffs between more than one conflicting objectives. Usually the population’s values of MOPSO algorithm are random which leads to random search quality. Particle Swarm Optimization Based on Multi Objective Functions with Uniform Design (MOPSO-UD), is proposed to enhance the accuracy of the pa...

Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but existing researches concentrate more on its application in single-objective optimization. In this paper, we focus on improving the algorithm to solve the multi-objective problem and increasing its convergence speed. The proposed algorithm is named as multi-objective Particle Swarm Marriage in Honey Bees Optimizati...

Particle swarm optimization cannot guarantee convergence to the global optimum on multi-modal functions, so multiple swarms can be useful. One means to coordinate these swarms is to use a separate search mechanism to identify different regions of the solution space for each swarm to explore. The expectation is that these independent subswarms can each perform an effective search around the regi...

Recently, the scheduling problem in distributed data-intensive computing environments has been an active research topic. This Chapter models the scheduling problem for work-flow applications in distributed dataintensive computing environments (FDSP) and makes an attempt to formulate the problem. Several meta-heuristics inspired from particle swarm optimization algorithm are proposed to formulat...

Data clustering is concerned with the division of a set of objects into groups of similar objects. In social insects there are many examples of clustering processes. Brood sorting observed in ant colonies can be considered as clustering according to the developmental state of the larvae. Also nest cleaning by forming piles of corpse or items is another example. These observed sorting and cluste...

To overcome the shortcomings of standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, whole population divided into two kinds sub-swarms: several same-sized sub-swarms sub-swarm. The achieve information interaction sharing among themselves through r...

This paper presents a new approach to control robots, which can quickly find their swarm while tracking a moving target through the obstacles of the environment. In this approach, an artificial potential field is generated between each free-robot and the virtual attractive point of the swarm. This artificial potential field will lead free-robots to their swarm. The swarm-finding of these freero...