Particle swarm optimization is affected by premature convergence, no guarantee in finding optimal solution, lack of solution amongst other issues. This paper reviews many literature on PSO and proposes a Hybrid MultiSearch Sub-Swarm PSO by using multiple sub swarm PSO in combination with multi search space algorithm. The particles are divided into equal parts and deployed into the number of sub...

Swarm robotics is a relatively new field that focuses on controlling large-scale homogeneous multi-robot systems. We survey six example swarm robotics control algorithms to give a brief overview of the current state of the cutting-edge. Experimental results show that swarm robotics algorithms are scalable, fault tolerant, robust and efficient. These favorable results stem from the algorithms be...

In light of the accuracy of particle swarm optimization-particle filter (PSO-PF) inadequate for multi-robot cooperative positioning, the paper presents population density particle swarm optimization-particle filter (PDPSO-PF), which draws cooperative coevolutionary algorithm in ecology into particle swarm optimization. By taking full account of the competitive relationship between the environme...

Swarm Microrobotics aims to apply Swarm Intelligence algorithms and strategies to a large number of fabricated miniaturized autonomous or semi-autonomous agents, allowing collective, decentralized and self-organizing behaviors of the robots. The ability to establish basic information networking is fundamental in such swarm systems, where inter-robot communication is the base of emergent behavio...

This Paper represents a literature review of Swarm intelligence algorithm in the area of semi-supervised classification. There are many research papers for applying swarm intelligence algorithms in the area of machine learning. Some algorithms of SI are applied in the area of ML either solely or hybrid with other ML algorithms. SI algorithms are also used for tuning parameters of ML algorithm, ...

An improved particle swarm optimization (IPSO) is used to solve economic dispatch problem (EDP). The IPSO has two position updating strategies. In the early stage of iteration, the individual in the population updates the position according to its own best experience with a large probability. In the later stage of iteration, the individual updates the position according to the best experience i...

The back propagation (BP) neural networks have been commonly used for automatic modulation recognition since the late 1990s. However, the back propagation algorithm easily falls into local minimum and the network learning is sensitive to initial weight values which usually determined by experience. The particle swarm optimization (PSO) algorithm is a global heuristic searching technology. By co...

This paper investigates an optimization procedure for the design of a synchronous motor (SM) using a particle swarm optimization (PSO) procedure. The PSO is proposed to minimize the motor volume and to maximize the motor output power. The proposed procedure has two stages for motor design. In the first stage, the stator parameters are optimized while in the second stage, the field and damper wi...

During reproductive swarming, honey bee scouts perform two very important functions. Firstly, they find new nesting locations and return to the swarm cluster to communicate their discoveries. Secondly, once the swarm is ready to depart, informed scout bees act as guides, leading the swarm to its final destination. We have previously hypothesised that the two processes, selecting a new nest site...