Genetic algorithm to solve constrained routing problem with applications for cruise missile routing

Abstract

In this paper the use of a Genetic Algorithm to solve a constrained vehicle routing problem is explored. The problem is two-dimensional with obstacles represented as ellipses of uncertainty surrounding each obstacle point. A route is defined as a series of points through which the vehicle sequentially travels from the starting point to the ending point. The physical constraints oftotal route length and maximum turn angle are included and appear in the fitness function. In order to be valid, a route must go from start to finish without violating any constraint. The effects that different mutation rates and population sizes have on the algorithm's computation speed and ability to find a high quality route are also explored. Finally, possible applications ofthis algorithm to the problem ofroute planning for cruise missiles are discussed.

An optimal algorithm for the obstacle neutralization problem

“…Similar to [16], the lagrangian dual of the problem is used where in order to find the best lagrange multiplier value, bisection method is used. Latourell et al [20] used genetic algorithms to optimize minimum risk routing of military vehicles. In their model, the authors considered the limitation on the sharpness of the turns that the aircraft can make.…”

Autonomous and cooperative robotic behavior based on fuzzy logic and genetic programming

“…The AUP fuzzy decision tree requires little CPU time and can produce effective decisions even on slow legacy processors. Also, the onetime decisions made by the GA [8,27] or dynamic programming approaches [1,30] would be represented as numbers, not mathematical expressions. It would be difficult or impossible to extract explicit relationships between input quantities like reliability, risk-tolerance, etc., and output quantities like the ultimate assignment of a UAV to a path.…”

“…The PH fuzzy decision rule is a simple elegant mathematical relationship between various fuzzy concepts essential to collaboration. It makes explicit relationships that would be impossible to discern using a black box like a neural net [6] or an optimization procedure [1,5,8,11,12,27,28,30] that delivers only a numerical output like a GA or dynamic programming.…”

Evolutionary Approaches to Path Planning Through Uncertain Environments

“…One approach to handling environmental uncertainty is the Markov localization of Fox ([6], [7]). For EAbased methods, uncertainty handling was presented in a simple fashion by Latourell [8].…”