Authors:
Mahamed G. H. Omran
1
and
Ayed Salman
2
Affiliations:
1
Gulf University for Science and Technology, Kuwait
;
2
Kuwait University, Kuwait
Keyword(s):
Metaheuristics, Opposition-based Learning, Chaotic Search, Differential Evolution, Quadratic Interpolation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. CODEQ has successfully been used to solve different types of problems (e.g. constrained, integer-programming, engineering) with excellent results. In this paper, a new mutated vector based on quadratic interpolation (QI) is incorporated into CODEQ. The proposed method is compared with the original CODEQ and a differential evolution variant the uses QI on eleven benchmark functions. The results show that using QI improves both the efficiency and effectiveness of CODEQ.