Next Article in Journal
A Sharp Rellich Inequality on the Sphere
Next Article in Special Issue
The Importance of Transfer Function in Solving Set-Union Knapsack Problem Based on Discrete Moth Search Algorithm
Previous Article in Journal
Probabilistic Interpretation of Solutions of Linear Ultraparabolic Equations
Previous Article in Special Issue
Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm
Open AccessArticle

A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization

School of Electrical Engineering & Automation, Jiangsu Normal University, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Mathematics 2018, 6(12), 287; https://doi.org/10.3390/math6120287
Received: 28 October 2018 / Revised: 17 November 2018 / Accepted: 19 November 2018 / Published: 27 November 2018
(This article belongs to the Special Issue Evolutionary Computation)
In order to overcome the several shortcomings of Particle Swarm Optimization (PSO) e.g., premature convergence, low accuracy and poor global searching ability, a novel Simple Particle Swarm Optimization based on Random weight and Confidence term (SPSORC) is proposed in this paper. The original two improvements of the algorithm are called Simple Particle Swarm Optimization (SPSO) and Simple Particle Swarm Optimization with Confidence term (SPSOC), respectively. The former has the characteristics of more simple structure and faster convergence speed, and the latter increases particle diversity. SPSORC takes into account the advantages of both and enhances exploitation capability of algorithm. Twenty-two benchmark functions and four state-of-the-art improvement strategies are introduced so as to facilitate more fair comparison. In addition, a t-test is used to analyze the differences in large amounts of data. The stability and the search efficiency of algorithms are evaluated by comparing the success rates and the average iteration times obtained from 50-dimensional benchmark functions. The results show that the SPSO and its improved algorithms perform well comparing with several kinds of improved PSO algorithms according to both search time and computing accuracy. SPSORC, in particular, is more competent for the optimization of complex problems. In all, it has more desirable convergence, stronger stability and higher accuracy. View Full-Text
Keywords: particle swarm optimization; confidence term; random weight; benchmark functions; t-test; success rates; average iteration times particle swarm optimization; confidence term; random weight; benchmark functions; t-test; success rates; average iteration times
Show Figures

Figure 1

MDPI and ACS Style

Zhang, X.; Zou, D.; Shen, X. A Novel Simple Particle Swarm Optimization Algorithm for Global Optimization. Mathematics 2018, 6, 287.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop