Automatic differentiation based for particle swarm optimization Steepest descent direction

Abstract

I. IntroductionThe Particle swarm Optimization (PSO) [1] is a population-based, self adaptive search optimization method motivated by the observation of simplified animal social behavior. It is becoming very popular due to its simplicity of implementation and ability to quickly converge to reasonably good solution [2]- [4]. Especially, global search capability of the method is very powerful. The particle swam optimization utilizes common knowledge of the group and individual experience effectively. That is, direction for the best estimator that a particle has ever reached, direction for the best one that all particle have ever found and momentum are successfully combined to determine the next iteration. Unfortunately, PSO show some weakness in term of balance between exploitation and exploration during the search [5]. For example in multi-objective problems, the search is not concentrated on the visited areas effectively, and often it shows a premature convergence and lack of diversification during moving from position to another. In order to solve this problem, various techniques have been proposed can be found in the literature [6], [7]. In most of the introduced techniques, extensive and intensive search are controlled by using the parameters setting. However this has an influence on the search for new solutions in case of multi-objective problems [7]. There is a popular technique which is used for evolutionary approaches, it is based on starting the search by an intensive search and then gradually explore other locations until all the search space is covered [8], [9]. However, such techniques make solving of multi-objective problems complicated especially in some situations where the search space contains many local optima.

Optimization pitch angle controller of rocket system using improved differential evolution algorithm

“…Simulated annealing algorithm is one of the examples of physical inspiration. The most commonly used is a biological inspiration, there is some algorithm based on biological inspiration such as ant colony optimization [5], particle swarm optimization [6,7], artificial bee colony [8] and differential evolution algorithm [9]. Differential evolution algorithm itself has solved tons of optimization problems.…”