Hybrid Genetic-simulated Annealing Algorithm for Optimal Weapon Allocation in Multilayer Defence Scenario

Abstract

Simulated annealing is one of the several heuristic optimisation techniques, that has been studied in the past to determine the most effective mix of weapons and their allocation to enemy
targets in a multilayer defence scenario. Simulated annealing is a general stochastic search algorithm. It is usually employed as an optimisation method to find a near-optimal solution for
hard combinatorial optimisation problems, but it is very difficult to give the accuracy of the  solution found. To find a better solution, aji often used strategy is to run the algorithm by
applying the existing best solution from the population space as the initial starting point. Giving many passes of genetic algorithm can generate the best start-point solution. This paper describes a new hybrid optimisation method, named genetic-simulated annealing, that combines the global crossover operators from genetic algorithm and the local stochastic hill-climbing features from simulated annealing, to arrive at an improved solution with reduced computational time. The basic idea is to use the genetic operators of genetic algorithm to quickly converge the search  to a near-global minima/maxima, that will further be refined to a near-optimum solution by simulated anneling using annealing process. The new hybrid algorithm has been applied to optimal weapon allocation in multilayer defence scenario problem to arrive at a better solution than produced by genetic algorithm or simulated annealing alone.