Introducing a new meta-heuristic algorithm based on See-See Partridge Chicks Optimization to solve dynamic optimization problems

The SSPCO (See-See Particle Chicks Optimization) is a type of swarm intelligence algorithm derived from the behavior of See-See Partridge. Although efficiency of this algorithm has been proven for solving static optimization problems, it has not yet been tested to solve dynamic optimization problems. Due to the nature of NP-Hard dynamic problems, this algorithm alone is not able to solve such optimization problems. Therefore, to enable the algorithm to optimally track the variable in these problems, it is necessary to be provided solutions with this algorithm so that can increase the performance of this algorithm for dynamic environments. In this paper, two solutions for combining SSPCO are presented: (1) the multi-swarm method and (2) memory with Gaussian density estimation. The problem with most multi-swarm methods is that as the population increases uncontrollably, the speed and efficiency of the algorithm gradually decreases. The multi-swarm methods presented in this paper is adapted to the problem space, and whenever there is a need to increase the population, a population is created adaptively, and this reduces the problems of previous methods. One of the issues that is being addressed to solve uncertainty problems is prediction of near future using data of the near past. In this article, to preserve past data a new memory called Gaussian density estimation memory is used. This memory fixes standard memory defects and improves the performance of the proposed algorithm. To evaluate the efficiency of the proposed method, the well-known moving peak benchmark function, which simulates behavior of dynamic problems, is used. The proposed algorithm is compared with the 10 most popular dynamic optimization algorithms. According to the experimental results, the proposed method reduces offline error to a great extent compared to other methods and the error produced by the proposed method is very small.