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Comprehensive Survey of the Hybrid Evolutionary Algorithms

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  • Wali Khan Mashwani

    (Department of Mathematics, Kohat University of Science & Technology (KUST), Kohat, Khyber Pukhtunkhwa, Pakistan)

Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) is 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.

Suggested Citation

  • Wali Khan Mashwani, 2013. "Comprehensive Survey of the Hybrid Evolutionary Algorithms," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 4(2), pages 1-19, April.
    • Handle: RePEc:igg:jaec00:v:4:y:2013:i:2:p:1-19
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    Cited by:

    1. Chetan Badgujar & Sanjoy Das & Dania Martinez Figueroa & Daniel Flippo, 2023. "Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review," Agriculture, MDPI, vol. 13(2), pages 1-39, January.
    2. A. S. Syed Shahul Hameed & Narendran Rajagopalan, 2022. "SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization," Mathematics, MDPI, vol. 10(5), pages 1-24, March.

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