Maximum Search Limitations: Boosting Evolutionary Particle Swarm Optimization Exploration

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Mário Serra Neto (Author) Other; Marco Mollinetti (Author) Other; Vladimiro Miranda (Author) FEUP; Leonel Carvalho (Author) Other

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 712-723

19th EPIA Conference on Artificial Intelligence, EPIA 2019

3 September 2019 through 6 September 2019

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00R-02J

DOI: 10.1007/978-3-030-30241-2_59

Abstract (EN): The following paper presents a novel strategy named Maximum Search Limitations (MS) for the Evolutionary Particle Swarm Optimization (EPSO). The approach combines EPSO standard search mechanism with a set of rules and position-wise statistics, allowing candidate solutions to carry a more thorough search around the neighborhood of the best particle found in the swarm. The union of both techniques results in an EPSO variant named MS-EPSO. MS-EPSO crucial premise is to enhance the exploration phase while maintaining the exploitation potential of EPSO. Algorithm performance is measured on eight unconstrained and two constrained engineering design optimization problems. Simulations are made and its results are compared against other techniques including the classic Particle Swarm Optimization (PSO). Lastly, results suggest that MS-EPSO can be a rival to other optimization methods. © Springer Nature Switzerland AG 2019.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12