Improved magnetic charged system search optimization algorithm with application to satellite formation flying Abstract   This paper is devoted to the implementation and application of an improved version of the metaheuristic algorithm called magnetic charged system search. Some modifications and novelties are introduced and tested. Firstly, the authorsxe2x80x99 attempt is to develop a selfadaptive and userfriendly algorithm which can automatically set all the preliminary parameters (such as the numbers of particles, the maximum iterations number) and the internal coefficients. Indeed, some mathematical laws are proposed to set the parameters and many coefficients can dynamically change during the optimization process based on the verification of internal conditions. Secondly, some strategies are suggested to enhance the performances of the proposed algorithm. A chaotic local search is introduced to improve the global best particle of each iteration by exploiting the features of ergodicity and randomness. Moreover, a novel technique is proposed to handle baddefined boundaries; in fact, the possibility to selfenlarge the boundaries of the optimization variables is considered, allowing to achieve the global optimum even if it is located on the boundaries or outside. The algorithm is tested through some benchmark functions and engineering design problems, showing good results, followed by an application regarding the problem of timesuboptimal manoeuvres for satellite formation flying, where the inverse dynamics technique, together with the Bsplines, is employed. This analysis proves the ability of the proposed algorithm to optimize control problems related to space engineering, obtaining better results with respect to more common and used algorithms in literature.

Balancing composite motion optimization Abstract   Metaheuristic algorithms play an important role in the optimization field thanks to their robustness and programming simplicity. Many metaheuristic methods have been devised in recent years. Inspired by nature, they usually simulate natural or humanspecific phenomena in a better way. A large amount of them are based on complicated behaviors requiring several implementation steps and algorithmspecific control parameters, which impedes users and limits solutions to different types of optimization problems. Hence, designing effective simple and parameterfree optimization methods attracts much attention. In this paper, we propose a novel populationbased optimization algorithm based on balancing composite motions (BCMO). The core idea is balancing composite motion properties of individuals in solution space. Equalizing global and local searches via a probabilistic selection model creates a movement mechanism of each individual. Four test suites selected in the literature, which vary from numerical benchmarks to practical problems, to demonstrate the performance of BCMO include: (1) 23 classical benchmark functions, (2) CEC 2005 benchmark functions, (3) CEC 2014 benchmark functions, and (4) 3 real engineering design problems. The statistical results reveal the promising performance and application of BCMO in a variety of optimization and practical problems with constrained and unknown search spaces.