The Determining the Flight Routes of Unmanned Aerial Vehicles Groups Based on Improved Ant Colony Algorithms

It is proposed to use an improved ant colony algorithm to determine the flight paths of unmanned aerial vehicles groups to the objects of intrest. A study was conducted on the application of the MAX-MIN Ant System to simultaneously determine the flight paths of several groups of unmanned aerial vehicles from different airfields to different objects of interest. Obstacles in the path of the unmanned aerial vehicles flight are also taken into account. As an example, the problem of a unmanned aerial vehicles breakthrough of an air defense system is considered. The number of unmanned aerial vehicles required to destroy the object of impact with a given probability is taken into account. The efficiency of the algorithm in the conditions of non - stationary environment is also investigated. Keywords— unmanned aerial vehicle, group, ant colony algorithm, route, flight, optimization

Particle swarm optimization for the shortest path problem

Solving the shortest path problem is very difficult in situations such as emergency rescue after a typhoon: road-damage caused by a typhoon causes the weight of the rescue path to be uncertain and impossible to represent using single, precise numbers. In such uncertain environments, neutrosophic numbers can express the edge distance more effectively: membership in a neutrosophic set has different degrees of truth, indeterminacy, and falsity. This paper proposes a shortest path solution method for interval-valued neutrosophic graphs using the particle swarm optimization algorithm. Furthermore, by comparing the proposed algorithm with the Dijkstra, Bellman, and ant colony algorithms, potential shortcomings and advantages of the proposed method are deeply explored, and its effectiveness is verified. Sensitivity analysis performed using a 2020 typhoon as a case study is presented, as well as an investigation on the efficiency of the algorithm under different parameter settings to determine the most reasonable settings. Particle swarm optimization is a promising method for dealing with neutrosophic graphs and thus with uncertain real-world situations.

Comparison between ABC and ACO: Tunning of On-Off MPPT for wind systems

In this work, a comparative study between two optimization algorithms for On-Off MPPT (Maximum Power Point Tracking) in wind power systems will be presented. The two optimizers considered in this paper are: the artificial bee colony algorithm (ABC) and the ant colony algorithms (ACO). Both of these optimization techniques are formulated to minimize different performance such as Integral Absolute Error (IAE) to determine optimal PI regulator values. In order to improve the performance and robustness properties of the proposed PI, the two tuning mechanisms are used in the optimization part. The system is modeled and tested under MATLAB/SIMULINK environment. The comparison of the performances, either by the test function or by MPPT, between these two optimizers shows the efficiency and superiority of the ABC-based approach proposed in terms of the qualities of the solution obtained, the speed of convergence, and the simplicity compared to ACO.