Heurestic Optimization-Based Trajectory Optimization

Conventional optimization methods have certain problems in finding the optimal solution. The feasible solution space of a trajectory optimization model may be constrained to a relatively limited corridor due to numerous mission-related constraints, easily leading to local minimum or infeasible solution identification. This section focuses on an attempt to use a biased particle swarm optimization method to solve the constrained trajectory design problem. By adding a normalized objective that reflects the entire quantity of constraint violations, the suggested method reformulates the original issue into an unconstrained multi-criteria version. The algorithm also includes a local exploration operation, a novel-bias selection method, and an evolution restart strategy to speed up progress during the evolutionary process. The success of the suggested optimization technique is confirmed by numerical simulation experiments that were generated from a confined atmospheric entry trajectory optimization example. Executing a number of comparative case studies also demonstrates the main benefits of the suggested strategy.