Swarming Behavior in Anopheles gambiae (sensu lato): Current Knowledge and Future Outlook

Effective management of insect disease vectors requires a detailed understanding of their ecology and behavior. In Anopheles gambiae sensu lato (s.l.) (Diptera: Culicidae) mating occurs during swarming, but knowledge of their mating behavior under natural conditions is limited. Mosquitoes mate in flight over specific landmarks, known as swarm markers, at particular locations. Swarms consist of males; the females usually approach the swarm and depart following copulation. The number of mating pairs per swarm is closely associated with swarm size. The shape and height of swarm markers vary and may depend on the environmental conditions at the swarm’s location. Male–male interactions in mosquito swarms with similar levels of attractive flight activity can offer a mating advantage to some individuals. Flight tone is used by mosquitoes to recognize the other sex and choose a desirable mate. Clarifying these and other aspects of mosquito reproductive behavior can facilitate the development of population control measures that target swarming sites. This review describes what is currently known about swarming behavior in Anopheles gambiae s.l., including swarm characteristics; mating within and outside of swarms, insemination in females, and factors affecting and stimulating swarming.

Constructing a cohesive pattern for collective navigation based on a swarm of robotics

Swarm robotics carries out complex tasks beyond the power of simple individual robots. Limited capabilities of sensing and communication by simple mobile robots have been essential inspirations for aggregation tasks. Aggregation is crucial behavior when performing complex tasks in swarm robotics systems. Many difficulties are facing the aggregation algorithm. These difficulties are as such: this algorithm has to work under the restrictions of no information about positions, no central control, and only local information interaction among robots. This paper proposed a new aggregation algorithm. This algorithm combined with the wave algorithm to achieve collective navigation and the recruitment strategy. In this work, the aggregation algorithm consists of two main phases: the searching phase, and the surrounding phase. The execution time of the proposed algorithm was analyzed. The experimental results showed that the aggregation time in the proposed algorithm was significantly reduced by 41% compared to other algorithms in the literature. Moreover, we analyzed our results using a one-way analysis of variance. Also, our results showed that the increasing swarm size significantly improved the performance of the group.

Negative updating applied to the best-of-n problem with noisy qualities

The ability to perform well in the presence of noise is an important consideration when evaluating the effectiveness of a collective decision-making framework. Any system deployed for real-world applications will have to perform well in complex and uncertain environments, and a component of this is the limited reliability and accuracy of evidence sources. In particular, in swarm robotics there is an emphasis on small and inexpensive robots which are often equipped with low-cost sensors more prone to suffer from noisy readings. This paper presents an exploratory investigation into the robustness of a negative updating approach to the best-of-n problem which utilises negative feedback from direct pairwise comparison of options and opinion pooling. A site selection task is conducted with a small-scale swarm of five e-puck robots choosing between $$n=7$$ n = 7 options in a semi-virtual environment with varying levels of sensor noise. Simulation experiments are then used to investigate the scalability of the approach. We now vary the swarm size and observe the behaviour as the number of options n increases for different error levels with different pooling regimes. Preliminary results suggest that the approach is robust to noise in the form of noisy sensor readings for even small populations by supporting self-correction within the population.

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems

This study introduces a novel strategy that can determine the optimal values of control parameters of a PSO. These optimal control parameters will be very valuable to all the online optimization problems where the convergence time and the failure convergence rate are vital concerns. The newly proposed strategy uses two nested PSO (NESTPSO) searching loops; the inner one contained the original objective function, and the outer one used the inner PSO as a fitness function. The control parameters and the swarm size acted as the optimization variables for the outer loop. These variables were optimized for the lowest premature convergence rate, the lowest number of iterations, and the lowest swarm size. The new proposed strategy can be used for all the swarm optimization techniques as well. The results showed the superiority of the proposed NESTPSO control parameter determination when compared with several state of the art PSO strategies.

Numerical Study of Optimal Location of Non-Circular Segmented Failure Surface in Soil Slope With Weak Soil Layer

The article addresses stability analysis of complicated slopes having weak soil layer sandwiched between two strong layers. The search for critical failure surface and associated optimum/minimum factor of safety (FOS) among all potential failure surfaces can be posed as an optimization problem. Two different variants of particle swarm optimization (PSO) models, namely inertia weight-based PSO (IW-PSO) and contemporary standard PSO (CS-PSO), are used to obtain optimum global solution. Detailed comparison between the global optimum solutions obtained from two PSO variants and the effect of swarm size is studied. The performance of IW-PSO and CS-PSO are studied by observing the convergence behavior of the respective algorithms with respect to iteration count. The influence of velocity clamping on the optimized solution is investigated and its use is found beneficial as it prevents the solution from overflying the region with global best solution. The studies related to swarm diversity demonstrating the exploitation and exploration behaviors of the algorithms are also presented.

Development of a Coupled TRNSYS-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System

An integrated electrical and thermal residential renewable energy system consisting of solar thermal collectors, gas boiler, fuel cell combined heat and power, a photovoltaic system with battery, inverter, and thermal storage for a single-family house of Sonnenhaus standard is investigated with a model predictive controller (MPC). The main focus of this article is to define a multi-objective mathematical function, develop a coupled simulation framework for the nonlinear time-varying deterministic discrete-time problem of the energy system using TRNSYS and MATLAB. With the developed methodology, a sensitivity analysis of maximum optimization time, swarm (or population or mesh) size of a typical spring day and a typical summer day assuming a 100% accurate weather and load forecast with three different algorithms: particle swarm optimization (PSO), genetic algorithm (GA) and global pattern search (GPS) are analyzed. Finally, the obtained results are compared with a status quo controller. Results show that the PSO algorithm optimizer performs the best in this MPC for such a complex and time-consuming MPC model in both the spring day and the summer day. The obtained results show that the PSO with swarm size 50 in the selected typical spring day and the PSO with swarm size 40 in the selected summer day reduces the objective function’s fitness value from 413 to −177 within 6 h optimization time and from 1396 to 1090 in 4 h optimization time respectively.

Cargo capture and transport by colloidal swarms

Controlling active colloidal particle swarms could enable useful microscopic functions in emerging applications at the interface of nanotechnology and robotics. Here, we present a computational study of controlling self-propelled colloidal particle propulsion speeds to cooperatively capture and transport cargo particles, which otherwise produce random dispersions. By sensing swarm and cargo coordinates, each particle’s speed is actuated according to a control policy based on multiagent assignment and path planning strategies that navigate stochastic particle trajectories to targets around cargo. Colloidal swarms are shown to dynamically cage cargo at their center via inward radial forces while simultaneously translating via directional forces. Speed, power, and efficiency of swarm tasks display emergent coupled dependences on swarm size and pair interactions and approach asymptotic limits indicating near-optimal performance. This scheme exploits unique interactions and stochastic dynamics in colloidal swarms to capture and transport microscopic cargo in a robust, stable, error-tolerant, and dynamic manner.