The simulated annealing strategy optimization immune algorithm based on MATLAB solves the problem of cooperative optimization and allocation of UAVs
The collaborative optimal allocation problem of UAVs is an important and complex optimization problem, involving task allocation and path planning of multiple UAVs. In order to effectively solve this problem, we can combine the simulated annealing strategy and the immune algorithm for optimization. This article will introduce how to use MATLAB to write code to implement this optimization process.
First, let's define the context and goals of the problem. Consider a fleet of drones that need to perform a series of tasks, each with a specific location and time window. Our goal is to find the optimal UAV task allocation and path planning scheme to minimize the overall execution time and energy consumption.
Next, we will introduce the basic principles of simulated annealing strategy and immune algorithm, and combine them to solve the UAV collaborative optimal allocation problem.
The simulated annealing strategy is a probability-based global optimization algorithm that simulates the atomic motion during the solid annealing process to search for the optimal solution. The algorithm avoids falling into local optimal solutions by accepting the probability of poorer solutions, thereby helping to find the global optimal solution in the search space.
The immune algorithm is an optimization algorithm developed inspired by the biological immune system. It achieves optimization by simulating processes such as antibody selection, cloning and mutation in the immune system. The immune algorithm has strong global search capabilities and fast convergence.
The following is a code example using MATLAB to implement simulated annealing strategy optimization immune algorithm:
% 参数设置
T0 = 100; % 初始温度
Tf = 1;