Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

Abstract

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its Application

“…;d. Hybrid algorithm, combining the advantages of the previous three algorithms to form a hybrid algorithm to solve high-latitude multi-objective complex optimization problems, for instance, a hybrid algorithm HCGA-PSO is proposed based on the global search ability of genetic algorithm and the fast convergence performance of PSO algorithm (Li et al, 2019), while Dang (Dang et al, 2016) proposed an analytical approach based on Newton's methods and nonlinear barrier method to solve this large-scale joint multi-objective optimization problem.…”

Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

“…As the technology improves, there is a substantial increase in the quantity of data, which has intensified the need to reform a new effective technique to speed up the search for compatible DNA sequences in a large data collection (Li et al, 2019;Hiraishi, 2019). One of the main problems of matching approaches is the variability in the length of sequences in a given sample, which will affect the results.…”