Search Strategies and Specifications in a Swarm versus Swarm Context

SUMMARY This paper proposes and evaluates swarming mechanisms of patrolling unmanned aerial vehicles (UAVs) that can collectively search a region for intruding UAVs. The main contributions include the development of multi-objective searching strategies and investigation of the required sensor configurations for the patrolling UAVs. Numerical results reveal that it is sometimes better to search through a region with a single swarm rather than multiple swarms deployed over sub-regions. Moreover, a large communication range does not necessarily improve search performances, and the patrolling swarm must have a speed close to the speed of the intruding UAVs to maximize the search performances.

A Novel Multiobjective Optimization for Cement Stabilized Soft Soil based on Artificial Bee Colony

Cement is the most widely used additive in soft soil stabilization due to its high strength and availability. The cement content and curing time have a direct influence on the stabilization cost and hence it is prudent to minimize these variables to achieve optimality. Thus, it is a classical multi-objective optimization problem to find the optimum combination of cement content used and the curing time provided to achieve the target strength. This paper brings out the use of Vector Evaluated Artificial Bee Colony (VEABC) algorithm, a multi-objective variant of Artificial Bee Colony (ABC) technique, for the problem on hand. VEABC is a swarm intelligence algorithm, which employs multiple swarms to handle the multiple objectives and the information migration between these swarms ensures a global optimum solution is reached. Due to the stochastic nature of ABC algorithm, the resulting Pareto Curve will cover a good range of data with smooth transition. The Pareto fronts obtained for target strength could be used as calibration charts for scheduling the soft soil stabilization activities.

Video Superresolution via Parameter-Optimized Particle Swarm Optimization

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality.

A Feasibility Study for Microwave Breast Cancer Detection Using Contrast-Agent-Loaded Bacterial Microbots

We propose a new approach to microwave breast tumor sensing and diagnosis based on the use of biocompatible flagellated magnetotactic bacteria (MTB) adapted to operate in human microvasculature. It has been verified experimentally by Martel et al. that externally generated magnetic gradients could be applied to guide the MTB along preplanned routes inside the human body, and a nanoload could be attached to these bacterial microbots. Motivated by these useful properties, we suggest loading a nanoscale microwave contrast agent such as carbon nanotubes (CNTs) or ferroelectric nanoparticles (FNPs) onto the MTB in order to modify the dielectric properties of tissues near the agent-loaded bacteria. Subsequently, we propose a novel differential microwave imaging (DMI) technique to track simultaneously multiple swarms of MTB microbots injected into the breast. We also present innovative strategies to detect and localize a breast tissue malignancy and estimate its size via this DMI-trackable bacterial microrobotic system. Finally, we use an anatomically realistic numerical breast phantom as a platform to demonstrate the feasibility of this tumor diagnostic method.

A Multi-Swarm Cooperative Perturbed Particle Swarm Optimization

Combined with a variety of ideas a Multi-swarm cooperative Perturbed Particle Swarm Optimization algorithm (MpPSO) is presented to improve the performance and to reduce the premature convergence of PSO. This algorithm includes the idea of multiple swarms to improve the evolution efficiency by information sharing between populations to avoid falling into local optimum caused by single population. It also includes the idea of perturbing the swarms beside the global best solution, which can escape from local optimum. Experiments show that the proposed algorithm MpPSO has better performance, better convergence and stability when comparing with the traditional and the recently improved particle swarm optimization.