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Comprehensive Survey of the Hybrid Evolutionary Algorithms

Comprehensive Survey of the Hybrid Evolutionary Algorithms

Wali Khan Mashwani
Copyright: © 2015 |Pages: 22
ISBN13: 9781466674561|ISBN10: 1466674563|EISBN13: 9781466674578
DOI: 10.4018/978-1-4666-7456-1.ch015
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MLA

Mashwani, Wali Khan. "Comprehensive Survey of the Hybrid Evolutionary Algorithms." Research Methods: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2015, pp. 322-343. https://doi.org/10.4018/978-1-4666-7456-1.ch015

APA

Mashwani, W. K. (2015). Comprehensive Survey of the Hybrid Evolutionary Algorithms. In I. Management Association (Ed.), Research Methods: Concepts, Methodologies, Tools, and Applications (pp. 322-343). IGI Global. https://doi.org/10.4018/978-1-4666-7456-1.ch015

Chicago

Mashwani, Wali Khan. "Comprehensive Survey of the Hybrid Evolutionary Algorithms." In Research Methods: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 322-343. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7456-1.ch015

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Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) are two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization problems (MOPs. The mathematical formulation of a MOP and some basic definitions for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs. The authors present literature review in subsections. Subsection 2.1 provides memetic multiobjective evolutionary algorithms. Subsection 2.2 presents the hybrid versions of well-known Pareto dominance based MOEAs. Subsection 2.4 summarizes some enhanced Versions of MOEA/D paradigm. Subsection 2.5 reviews some multimethod search approaches dealing optimization problems.

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