Improving multi-criterion optimization with chaos: a novel Multi-Objective Chaotic Crow Search Algorithm

This paper presents two multi-criteria optimization techniques: the Multi-Objective Crow Search Algorithm (MOCSA) and an improved chaotic version called Multi-Objective Chaotic Crow Search Algorithm (MOCCSA). Both methods MOCSA and MOCCSA are based on an enhanced version of the recently published Cr...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Neural computing & applications Ročník 29; číslo 8; s. 319 - 335
Hlavní autori: Hinojosa, Salvador, Oliva, Diego, Cuevas, Erik, Pajares, Gonzalo, Avalos, Omar, Gálvez, Jorge
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.04.2018
Springer Nature B.V
Predmet:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Criteria
Data Mining and Knowledge Discovery
Hatches
Image Processing and Computer Vision
Multiple objective analysis
Optimization techniques
Original Article
Particle swarm optimization
Probability and Statistics in Computer Science
Search algorithms
Crow Search Algorithm
Multi-Objective optimization
Evolutionary algorithms
Chaotic maps
ISSN:0941-0643, 1433-3058
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper presents two multi-criteria optimization techniques: the Multi-Objective Crow Search Algorithm (MOCSA) and an improved chaotic version called Multi-Objective Chaotic Crow Search Algorithm (MOCCSA). Both methods MOCSA and MOCCSA are based on an enhanced version of the recently published Crow Search Algorithm. Crows are intelligent animals with interesting strategies for protecting their food hatches. This compelling behavior is extended into a Multi-Objective approach. MOCCSA uses chaotic-based criteria on the optimization process to improve the diversity of solutions. To determinate if the performance of the algorithm is significantly enhanced, the incorporation of a chaotic operator is further validated by a statistical comparison between the proposed MOCCSA and its chaotic-free counterpart (MOCSA) indicating that the results of the two algorithms are significantly different from each other. The performance of MOCCSA is evaluated by a set of standard benchmark functions, and the results are contrasted with two well-known algorithms: Multi-Objective Dragonfly Algorithm and Multi-Objective Particle Swarm Optimization. Both quantitative and qualitative results show competitive results for the proposed approach.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-3251-x