Next Article in Journal
Separation of Light Liquid Paraffin C5–C9 with Cuban Volcanic Glass Previously Used in Copper Elimination from Water Solutions
Next Article in Special Issue
Artificial Flora (AF) Optimization Algorithm
Previous Article in Journal
Numerical Study on the Seismic Performance of a Steel–Concrete Hybrid Supporting Structure in Thermal Power Plants
Previous Article in Special Issue
3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(2), 293; doi:10.3390/app8020293

Parallel Technique for the Metaheuristic Algorithms Using Devoted Local Search and Manipulating the Solutions Space

1
Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
2
Department of Software Engineering, Kaunas University of Technology, Studentu 50, LT-51368, Kaunas, Lithuania
*
Author to whom correspondence should be addressed.
Received: 16 December 2017 / Revised: 9 February 2018 / Accepted: 13 February 2018 / Published: 16 February 2018
(This article belongs to the Special Issue Swarm Robotics)
View Full-Text   |   Download PDF [445 KB, uploaded 24 February 2018]   |  

Abstract

The increasing exploration of alternative methods for solving optimization problems causes that parallelization and modification of the existing algorithms are necessary. Obtaining the right solution using the meta-heuristic algorithm may require long operating time or a large number of iterations or individuals in a population. The higher the number, the longer the operation time. In order to minimize not only the time, but also the value of the parameters we suggest three proposition to increase the efficiency of classical methods. The first one is to use the method of searching through the neighborhood in order to minimize the solution space exploration. Moreover, task distribution between threads and CPU cores can affect the speed of the algorithm and therefore make it work more efficiently. The second proposition involves manipulating the solutions space to minimize the number of calculations. In addition, the third proposition is the combination of the previous two. All propositions has been described, tested and analyzed due to the use of various test functions. Experimental research results show that the proposed methodology for parallelization and manipulation of solution space is efficient (increasing the accuracy of solutions and reducing performance time) and it is possible to apply it also to other optimization methods. View Full-Text
Keywords: optimization; meta-heuristic; parallel technique optimization; meta-heuristic; parallel technique
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Połap, D.; Kęsik, K.; Woźniak, M.; Damaševičius, R. Parallel Technique for the Metaheuristic Algorithms Using Devoted Local Search and Manipulating the Solutions Space. Appl. Sci. 2018, 8, 293.

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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top